KR102537471B1 - Method and device for deep-learning based computation time management of go game service - Google Patents

Abstract

An apparatus for managing operation time of a Go game service based on deep learning according to an embodiment of the present invention includes a communication unit receiving at least one of a value value and a relative starting time according to a state of a Go board; a memory for storing the time adjustment unit; and a processor which reads the time adjustment unit and controls the time adjustment unit to determine a start preparation time using at least one of the value value and the relative start time.

Description

Deep learning-based Go game service computation time management method and device thereof

The present invention relates to a deep learning-based Go game service operation time management method and apparatus therefor. More specifically, it relates to a deep learning-based Go game service calculation time management method and apparatus for managing deep learning calculation time according to the match situation of the Go game service.

As the use of user terminals such as smartphones, tablet PCs, PDAs (Personal Digital Assistants), laptops, etc. has become popular and information processing technology has developed, it has become possible to play Go, a kind of board game, using user terminals. It became possible to play Go with programmed artificial intelligence computers.

Compared to other board games such as chess or chess, Go has a large number of cases, so artificial intelligence computers have limitations in playing a game at the human level, and research to increase the energy of artificial intelligence computers is actively progressing.

Recently, developers have applied the Monte Carlo Tree Search (MCTS) algorithm and deep learning technology to artificial intelligence computers to raise the power of artificial intelligence computers to the level of professional players.

Also, Go is a time-limited board game. The time is different for each Go tournament, but each player can be given various times from 1 hour to 5 hours, and when the given time is exceeded, a countdown rule is applied, and there is a rule to lose if the number of countdowns is exceeded.

Therefore, knowing the remaining Go time and determining how much time is spent on a single move is an important factor in winning the game.

However, artificial intelligence computers have a problem in that the amount of time spent to make a move is always constant, and thus, in an important phase, a bad move is made.

In addition, general, amateur, or artificial intelligence computers generally have a problem in that they cannot set up a time strategy because they cannot predict the length of the remaining game.

JP 4392621 B2

An object of the present invention is to provide a deep learning-based Go game service operation time management method and apparatus for managing the deep learning operation time according to the game situation on the Go game service.

In detail, an object of the present invention is to provide a deep learning-based Go game service operation time management method and apparatus for changing the start preparation time in an important phase in consideration of the change in odds or the opponent's start time.

In addition, an object of the present invention is to provide a deep learning-based Go game service operation time management method and apparatus capable of effectively dividing the starting preparation time using the predicted remaining game length.

However, the technical problems to be achieved by the present invention and the embodiments of the present invention are not limited to the technical problems described above, and other technical problems may exist.

An apparatus for managing operation time of a Go game service based on deep learning according to an embodiment of the present invention includes a communication unit receiving at least one of a value value and a relative starting time according to a state of a Go board; a memory for storing the time adjustment unit; and a processor which reads the time adjustment unit and controls the time adjustment unit to determine a start preparation time using at least one of the value value and the relative start time.

At this time, the time adjustment unit determines at least one of a change trend and a change range of the value, and determines the start preparation time according to the determined change trend and change range.

In addition, the time adjustment unit increases the start preparation time when the change rate of the value value, which is the change trend of the value value, is less than a predetermined value.

In addition, the time adjustment unit increases the start preparation time when the range of change between the value value in the previous start step and the current value value decreases by more than a predetermined value.

In addition, the time adjustment unit, based on the relative start time, the relative start time immediately before the start, which is the relative start time required for the other party's previous start, and the relative start time required for the plurality of starts performed by the other party during a predetermined point in time. Calculate the average relative start time, which is the average.

In addition, the time adjustment unit increases the set-out preparation time when the previous relative set-off time is greater than the average relative set-out time.

In addition, the time adjustment unit, if the conditions for increasing the set-out preparation time are satisfied, calculates the set-out time increment which is a criterion for increasing the set-out preparation time.

In addition, the memory further includes a time management unit providing a basic start time that is a preset start preparation time, and the time adjustment unit determines the start time increment based on the basic start time provided by the time management unit.

In addition, the time adjustment unit determines the start time increment based on the preset countdown time and the basic start time.

In addition, the memory further includes a time management unit providing a basic start time that is a preset start preparation time, and the time adjustment unit is a larger start time of the previous relative start time and the basic start time provided by the time management unit. is determined as the launch preparation time.

In addition, the time adjustment unit calculates a relative start time ratio that is the ratio of the previous relative start time to the average relative start time, and the memory further includes a time management unit that provides a basic start time that is a preset start preparation time, The time adjustment unit determines the start preparation time by performing a predetermined operation based on the relative start time ratio and the basic start time provided by the time management unit, when the relative start time ratio satisfies a predetermined criterion.

On the other hand, the deep learning-based Go game service calculation time management method according to an embodiment of the present invention is a method for managing the deep learning-based Go game service calculation time in a time management model server, and the value value and relative value according to the checkerboard state Receiving at least one of start times; and determining a start preparation time using at least one of the received value value and the relative start time, wherein the step of determining the start preparation time comprises determining at least one of a change trend and a range of change in the value value. and determining the start preparation time according to the determined change trend and the range of change, and based on the relative start time, the previous relative start time, which is the relative start time required for the other party's previous start, and the other party's predetermined start time. Calculating an average relative start time, which is an average of relative start times required for a plurality of starts performed during the time point, and determining the start preparation time based on the calculated relative start time immediately before and the average relative start time include

The deep learning-based Go game service operation time management method and apparatus according to an embodiment of the present invention can change the starting preparation time in an important phase.

In addition, the deep learning-based Go game service operation time management method and apparatus according to an embodiment of the present invention can predict the remaining game time.

In addition, the deep learning-based Go game service operation time management method and apparatus according to an embodiment of the present invention can effectively distribute the start preparation time using the predicted remaining game time.

However, the effects obtainable in the present invention are not limited to the effects mentioned above, and other effects not mentioned can be clearly understood from the description below.

1 is an exemplary view of a deep learning-based Go game service system according to an embodiment of the present invention.
2 is a diagram for explaining the structure of an initiation model of an initiation model server for an initiation of an artificial intelligence computer in a deep learning-based Go game service according to an embodiment of the present invention.
3 is a diagram for explaining a movement probability distribution for an initiation point according to an initiation model policy.
4 is a diagram for explaining the value of the starting point of the starting model and the number of visits.
5 is a diagram for explaining a process in which an initiation model is initiated according to a pipeline of a search unit.
6 is an exemplary diagram showing a screen providing a situation determination function of a deep learning-based Go game service according to an embodiment of the present invention.
7 is a diagram for explaining the structure of the situation judgment model of the situation judgment model server of the present invention.
8 is a diagram for explaining one block of a neural network structure composed of a plurality of blocks of the position judgment model of the present invention.
9 is a diagram for explaining first and second preprocessing steps for generating correct answer labels used to learn a situation judgment model of the present invention.
10 is a diagram for explaining first and second pre-processing steps for generating correct answer labels used to learn a position judgment model of the present invention.
11 is a diagram for explaining a third preprocessing step for generating a correct answer label used to learn a position judgment model of the present invention.
12 is a diagram for explaining the result of the situation judgment of the situation judgment model of the present invention.
13 is a comparison between the situation judgment result of the situation judgment model of the present invention and the situation judgment result by the deep learning model according to the prior art.
14 is a comparison between the situation judgment result of the situation judgment model of the present invention and the situation judgment result by the deep learning model according to the prior art.
15 is a comparison between the situation judgment result of the situation judgment model of the present invention and the situation judgment result of the deep learning model according to the prior art.
16 is an exemplary diagram of a signal flow in a deep learning-based Go game service system according to an embodiment of the present invention.
17 is a situation judgment method among deep learning-based Go game service methods according to an embodiment of the present invention.
18 is a preprocessing method of training data for generating a correct answer label in the situation judgment method of FIG. 17 .
19 is a diagram for explaining a time management unit of a time management model server according to an embodiment of the present invention.
20A and 20B are diagrams for explaining distributed calculation of a time management unit according to an embodiment of the present invention.
21 is an exemplary diagram of a signal flow in a Go game service system of a time management model server according to an embodiment of the present invention.
22 is a method for determining start preparation time among deep learning-based Go game service methods according to an embodiment of the present invention.
23 is a diagram for explaining a time adjustment unit of a time management model server according to another embodiment of the present invention.
24 is a method for determining start preparation time among deep learning-based Go game service methods according to another embodiment of the present invention.
25 is an example of a diagram for explaining a method of determining a start preparation time according to a change trend of a value according to another embodiment of the present invention.
26 is an example of a diagram for explaining a method of determining a start preparation time according to a variation range of a value according to another embodiment of the present invention.
27 is a method for determining start preparation time among deep learning-based Go game service methods according to another embodiment of the present invention.
28 is an example of a diagram for explaining a method of determining a start preparation time according to a relative start time according to another embodiment of the present invention.
29 is a diagram for explaining a time management model of a time management model server according to another embodiment of the present invention.
30A and 30B are diagrams for explaining a collection change amount used to generate game time information according to another embodiment of the present invention.
31A and 31B are diagrams for explaining a collection change amount used to generate game time information according to another embodiment of the present invention.
32A and 32B are diagrams for explaining common multiples used to generate game time information according to another embodiment of the present invention.
33 is an exemplary diagram of a signal flow in a Go game service system of a time management model server according to another embodiment of the present invention.
34 is a method for generating game time information among deep learning-based Go game service methods according to another embodiment of the present invention.

Since the present invention can apply various transformations and have various embodiments, specific embodiments will be illustrated in the drawings and described in detail in the detailed description. Effects and features of the present invention, and methods for achieving them will become clear with reference to the embodiments described later in detail together with the drawings. However, the present invention is not limited to the embodiments disclosed below and may be implemented in various forms. In the following embodiments, terms such as first and second are used for the purpose of distinguishing one component from another component without limiting meaning. Also, expressions in the singular number include plural expressions unless the context clearly dictates otherwise. In addition, terms such as include or have mean that features or elements described in the specification exist, and do not preclude the possibility that one or more other features or elements may be added. In addition, in the drawings, the size of components may be exaggerated or reduced for convenience of explanation. For example, since the size and thickness of each component shown in the drawings are arbitrarily shown for convenience of description, the present invention is not necessarily limited to the illustrated bar.

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings, and when describing with reference to the drawings, the same or corresponding components are assigned the same reference numerals, and overlapping descriptions thereof will be omitted. .

1 is an exemplary view of a deep learning-based Go game service system according to an embodiment of the present invention.

Referring to FIG. 1, the deep learning-based Go game service system according to an embodiment includes a terminal 100, a Go server 200, a starting model server 300, a situation judgment model server 400, and a network 500. can include

Each component of FIG. 1 may be connected through a network 500 . It refers to a connection structure capable of exchanging information between nodes such as the terminal 100, the Go server 200, the start model server 300, the situation judgment model server 400, and the time management model server 500. , Examples of such networks include a 3rd Generation Partnership Project (3GPP) network, a Long Term Evolution (LTE) network, a World Interoperability for Microwave Access (WIMAX) network, the Internet, a Local Area Network (LAN), and a Wireless LAN (Wireless). Local Area Network), WAN (Wide Area Network), PAN (Personal Area Network), Bluetooth (Bluetooth) network, satellite broadcasting network, analog broadcasting network, DMB (Digital Multimedia Broadcasting) network, etc. are included, but are not limited thereto.

<Terminal (100)>

First, the terminal 100 is a terminal of a user who wants to receive a Go game service. Also, the terminal 100 is one or more computers or other electronic devices used by a user to execute applications that perform various tasks. For example, a computer, laptop computer, smart phone, mobile phone, PDA, tablet PC, or any other device operable to communicate with the Go server 200. However, it is not limited thereto, and the terminal 100 is executed on various machines, includes processing logic that interprets and executes commands stored in a plurality of memories, and provides a graphical user interface (GUI) on an external input/output device. It may contain various other elements, such as processes that display graphical information. In addition, the terminal 100 may be connected to an input device (eg, mouse, keyboard, touch sensitive surface, etc.) and an output device (eg, display device, monitor, screen, etc.). Applications executed by the terminal 100 may include game applications, web browsers, web applications running on web browsers, word processors, media players, spreadsheets, image processors, security software, or the like. .

In addition, the terminal 100 may include at least one memory 101 storing instructions, at least one processor 102 and a communication unit 103 .

The memory 101 of the terminal 100 may store a plurality of application programs or applications running in the terminal 100, data for operation of the terminal 100, and commands. The instructions are executable by the processor 102 to cause the processor 102 to perform operations, the operations sending a Go game execution request signal, sending and receiving game data, sending and receiving starting information, sending a situation judgment request signal, It may include operations for receiving a decision result, requesting game time information, receiving game time information, and receiving various types of information. In addition, the memory 101 may be a variety of storage devices such as ROM, RAM, EPROM, flash drive, hard drive, etc. in terms of hardware, and the memory 130 performs the storage function of the memory 101 on the Internet. It can also be a web storage that performs.

The processor 102 of the terminal 100 may perform data processing to receive a Go game service by controlling overall operations. When a Go game application is executed in the terminal 100, a Go game environment is configured in the terminal 100. In addition, the Go game application exchanges Go game data with the Go server 200 through the network 500 so that the Go game service is executed on the terminal 100 . These processors 102 include application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, and microcontrollers. It may be micro-controllers, microprocessors, or any type of processor for performing other functions.

The communication unit 103 of the terminal 100 uses the following communication methods (eg, Global System for Mobile communication (GSM), Code Division Multi Access (CDMA), High Speed Downlink Packet Access (HSDPA), High Speed Uplink (HSUPA)) Packet Access), LTE (Long Term Evolution), LTE-A (Long Term Evolution-Advanced), etc.), WLAN (Wireless LAN), Wi-Fi (Wireless-Fidelity), Wi-Fi (Wireless Fidelity) Direct, DLNA ( A wireless signal may be transmitted and received with at least one of a base station, an external terminal, and a server on a network constructed according to Digital Living Network Alliance (WiBro), Wireless Broadband (WiBro), and World Interoperability for Microwave Access (WiMAX).

This terminal 100 performs at least some of the functional operations performed by at least one of the Go server 200, the starting model server 300, the situation judgment model server 400, and the time management model server 500, which will be described later. You may.

<Go server (200)>

The Go game service provided by the Go server 200 may be configured in a form in which a virtual computer user provided by the Go server 200 and a real user participate in the game together. In the Go game environment implemented on the user-side terminal 100, one real user and one computer user play the game together. In another aspect, the Go game service provided by the Go server 200 may be configured in a form in which a plurality of user-side devices participate to play the Go game.

The Go server 200 may include at least one memory 201 storing instructions, at least one processor 202 and a communication unit 203.

The memory 201 of the Go server 200 may store a plurality of application programs or applications running in the Go server 200, data for the operation of the Go server 200, and commands. . Instructions are executable by the processor 202 to cause the processor 202 to perform operations, which include receiving a game execution request signal, transmitting and receiving game data, transmitting and receiving starting information, transmitting and receiving a situation judgment request signal, and transmitting and receiving a situation judgment result. , start preparation time transmission and reception, game information time transmission and reception, and various transmission operations. In addition, the memory 201 may store a plurality of notations or previously published notations that were played in the Go server 200. Each of the plurality of notation may include all information from the first start, which is the first start information of the start of the match, to the final start when the match ends. That is, a plurality of notes may include history information about the undertaking. The Go server 200 enables the position determination model server 400 to provide a plurality of notations stored for training of the position determination model server 400 . In addition, the memory 201 may be a variety of storage devices such as ROM, RAM, EPROM, flash drive, hard drive, etc. in terms of hardware, and the memory 201 performs the storage function of the memory 201 on the Internet. It can also be a web storage that performs.

The processor 202 of the Go server 200 may perform data processing to provide a Go game service by controlling overall operations. These processors 202 include application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, and microcontrollers. It may be micro-controllers, microprocessors, or any type of processor for performing other functions.

The Go server 200 may communicate with the terminal 100, the starting model server 300, and the situation judgment model server 400 via the network 500 through the communication unit 203.

<Initiation model server 300>

The undertaking model server 300 may include a separate cloud server or computing device. In addition, the start model server 300 may be a neural network system installed in the processor of the terminal 100 or the data processing unit of the Go server 200, but hereinafter, the start model server 300 is the terminal 100 or the Go server ( 200) and a separate device.

The undertaking model server 300 may include at least one memory 301 storing instructions, at least one processor 302 and a communication unit 303.

The starting model server 300 learns by itself according to the rules of Go, builds a deep learning model, the starting model, and is an artificial intelligence computer capable of playing a game with the user of the terminal 100. launch can be carried out. A detailed description of how the initiation model server 300 trains the initiation model follows the description of the initiation model of FIGS. 2 to 5 .

The memory 301 of the initiation model server 300 stores a plurality of application programs or applications running in the initiation model server 300, data for the operation of the initiation model server 300, and commands. can be saved Instructions are executable by the processor 302 to cause the processor 302 to perform operations, which include a set-up model learning (training) operation, set-up information transmission and reception, set-up preparation time reception, game time information reception, and various transmissions. Actions may be included. In addition, the memory 301 may store an undertaking model that is a deep learning model. In addition, the memory 301 may be a variety of storage devices such as ROM, RAM, EPROM, flash drive, hard drive, etc. in terms of hardware, and the memory 301 performs the storage function of the memory 301 on the Internet. It can also be a web storage that performs.

The processor 302 of the set-up model server 300 reads the set-out model stored in the memory 302, and performs the set-out model learning and Go game set-up described below according to the built neural network system. Depending on the embodiment, the processor 302 may be configured to include a main processor for controlling all units and a plurality of graphics processing units (GPUs) for processing large-capacity calculations required for driving a neural network according to an initiation model. there is.

The undertaking model server 300 may communicate with the Monarch server 200 via the network 500 through the communication unit 303 .

<Scenario judgment model server 400>

The situation judgment model server 400 may include a separate cloud server or a computing device. In addition, the situation judgment model server 400 may be a neural network system installed in the processor of the terminal 100 or the data processing unit of the Go server 200, but hereinafter, the situation judgment model server 400 is used for the terminal 100 or the Go server. It will be described as a device separate from the server 200.

The layout judgment model server 400 may include at least one memory 401 storing instructions, at least one processor 402 and a communication unit 403 .

The situation judgment model server 400 may receive a training data set from the Go server 200 through the communication unit 403 . The training data set may be a plurality of notations and situation determination information for the plurality of notations. The situation judgment model server 400 uses the received training data set to perform supervised learning to determine the situation of the state of the board on which the game of Go is placed, and builds a situation judgment model, which is a deep learning model, and determines the situation of the user of the terminal 100 Depending on the request for judgment, the situation may be judged. A detailed description of how the situation judgment model server 400 trains the situation judgment model follows the description of the situation judgment model of FIGS. 6 to 18 .

The memory 401 of the layout judgment model server 400 stores a plurality of application programs or applications running in the layout judgment model server 400 and data for the operation of the layout judgment model server 400. , can store commands. The instructions are executable by the processor 402 to cause the processor 402 to perform operations, the operations being a situation judgment model learning (training) operation, performing a situation judgment, transmitting a situation judgment result, receiving a plurality of notational information, It may include transmission of variation information of collections, transmission of common multiple information, and various transmission operations. In addition, the memory 401 may store a situation judgment model that is a deep learning model. In addition, the memory 401 may be a variety of storage devices such as ROM, RAM, EPROM, flash drive, hard drive, etc. in terms of hardware, and the memory 401 performs the storage function of the memory 301 on the Internet. It can also be a web storage that performs.

The processor 402 of the situation judgment model server 400 reads the situation judgment model stored in the memory 402, and performs learning of the situation judgment model described below and judgment of the situation of the checkerboard among the players according to the built neural network system. . According to an embodiment, the processor 402 is configured to include a main processor that controls all units and a plurality of graphics processing units (GPUs) that process large-capacity calculations required for driving a neural network according to a situation judgment model. can

The position judgment model server 400 may communicate with the Monarch server 200 via the network 500 through the communication unit 403 .

<Time management model server (500)>

The time management model server 500 may include a separate cloud server or a computing device. In addition, the time management model server 500 may be a neural network system installed in the processor of the terminal 100, the memory of the Go server 200, the memory of the start model server 300, or the memory of the situation judgment model server 400. Hereinafter, the time management model server 500 will be described as a device separate from the terminal 100, the Monarch server 200, the starting model server 300, or the situation judgment model server 400.

The time management model server 500 may include at least one memory 501 storing instructions, at least one processor 502 and a communication unit 503 .

In addition, the time management model server 500 may receive the number of visits, a search probability value, or a value value from the undertaking model server 300 through the communication unit 503 . The time management model server 500 may determine the start preparation time using the received number of visits, a search probability value, and a value value. A detailed description of the method for determining the start preparation time of the time management model server 500 follows the descriptions of FIGS. 19 to 22 .

In addition, the time management model server 500 may receive a training data set from the Go server 500 through the communication unit 503 . The training data set may be a plurality of notations, position judgment information for the plurality of notations, and time information according to each checkerboard state for the plurality of notations.

In addition, the time management model server 500 receives a value and/or the opponent's start preparation time (hereinafter, the opponent's start time) from the start model server 300 and/or the Go server 500 through the communication unit 503. can do. The time management model server 500 may determine the start preparation time using the received value and/or the relative start time. A detailed description of the method for determining the start preparation time of the time management model server 500 follows the descriptions of FIGS. 23 to 28 .

In addition, the time management model server 500 may receive situation determination information from the situation determination model server 400 through the communication unit 503 . The situation determination information may include change amount information of catchments, common multiple information, and the like. The time management model server 500 builds a time management model, which is a deep learning model, by conducting supervised learning to generate game time information using the received training data set, situation judgment information, value value, etc. ) or game time information according to the checkerboard state may be provided to the terminal 100 . A detailed description of how the time management model server 50 trains the time management model follows the description of the time management model of FIGS. 29 to 34 .

The memory 501 of the time management model server 500 stores data for the operation of a plurality of application programs or applications running in the time management model server 500 and the position judgment model server 400. , can store commands. Instructions are executable by the processor 502 to cause the processor 502 to perform operations, such as time management model learning (training) operations, receiving number of visits, search probabilities or values, and determining preparation time for launching. , It may include receiving a plurality of notation information, receiving change amount information of collections, receiving common multiple information, generating game time information, and various transmission operations. Also, the memory 501 may store a time management model that is a time management unit, a time adjustment unit, or a deep learning model. In addition, the memory 501 may be a variety of storage devices such as ROM, RAM, EPROM, flash drive, hard drive, etc. in terms of hardware, and the memory 501 performs the storage function of the memory 501 on the Internet. It can also be a web storage that performs.

The processor 502 of the time management model server 500 reads the time management unit stored in the memory 501 and determines the start preparation time described below.

In addition, the processor 502 of the time management model server 500 reads the time adjustment unit stored in the memory 501 and determines the start preparation time described later.

In addition, the processor 502 of the time management model server 500 reads the time management model stored in the memory 501 and generates game time information described below according to the constructed neural network system.

According to an embodiment, the processor 502 is configured to include a main processor that controls all units and a plurality of graphics processing units (GPUs) that process large-capacity calculations required for driving a neural network according to a time management model. can

The time management model server 500 may communicate with the Monarch server 200, the start model server 300, and the situation judgment model server 400 via the network 500 through the communication unit 403.

<Startup model>

2 is a diagram for explaining the start model structure of the start model server for the start of an artificial intelligence computer in the deep learning-based Go game service according to an embodiment of the present invention, and FIG. 3 is the start point according to the start model policy. Figure 4 is a diagram for explaining the movement probability distribution for , Figure 4 is a diagram for explaining the value value and the number of visits to the starting point of the initiation model, and Figure 5 shows the process initiating the initiation model according to the pipeline of the search unit. It is a drawing for explanation.

Referring to FIG. 2 , the initiation model according to an embodiment of the present invention is a deep learning model of the initiation model server 300, and may include a search unit 310, a self-play unit 320, and an initiation neural network 330. .

)을 출력할 수 있다. 셀프 플레이부(320)는 탐색 확률값(

)에 따라 스스로 바둑 대국을 할 수 있다. 셀프 플레이부(320)는 게임의 승패가 결정되는 시점까지 스스로 바둑 대국을 진행하고, 자가 대국이 종료되면 바둑판 상태(S), 탐색 확률값(

), 자가 플레이 가치값(z)을 착수 신경망(330)에 제공할 수 있다. 바둑판 상태(S)는 착수점들에 바둑돌이 놓여진 상태이다. 자가 플레이 가치값(z)은 바둑판 상태(S)에서 자가 대국을 하였을 때 승률 값이다. 착수 신경망(330)은 이동 확률값(p)과 가치값(V)을 출력할 수 있다. 이동 확률값(p)은 바둑판 상태(S)에 따라 착수점들에 대해 어느 착수점에 착수하는 것이 게임을 이길 수 있는 좋은 수인지 수치로 나타낸 확률분포값이다. 가치값(V)은 해당 착수점에 착수시 승률을 나타낸다. 예를 들어, 이동 확률값(p)이 높은 착수점이 좋은 수일 수 있다. 착수 신경망(330)은 이동 확률값(p)이 탐색 확률값(

)과 동일해지도록 트레이닝되고, 가치값(V)이 자가 플레이 가치값(z)과 동일해지도록 트레이닝될 수 있다. 이후 트레이닝된 착수 신경망(330)은 탐색부(310)를 가이드하고, 탐색부(310)는 이전 탐색 확률값(

)보다 더 좋은 수를 찾도록 착수 준비 시간 동안 MCTS를 진행하여 새로운 탐색 확률값(

)을 출력하게 한다. 예를 들어, 착수 준비 시간은 MCTS 진행 시간에 따라 평균 착수 준비 시간, 제1 착수 준비 시간 및 제2 착수 준비 시간 중 어느 하나의 착 수 준비 시간을 따를 수 있다. 착수 준비 시간은 시간 관리 모델 서버(500)에서 제공할 수 있고 기본적으로 평균 착수 준비 시간으로 설정되어 있을 수 있다. 셀프 플레이부(320)는 새로운 탐색 확률값(

)에 기초하여 바둑판 상태(S)에 따른 새로운 자가 플레이 가치값(z)을 출력하고 바둑판 상태(S), 새로운 탐색 확률값(

), 새로운 자가 플레이 가치값(z)을 착수 신경망(330)에 제공할 수 있다. 착수 신경망(330)은 이동 확률값(p)과 가치값(V)이 새로운 탐색 확률값(

)과 새로운 자가 플레이 가치값(z)으로 출력되도록 다시 트레이닝될 수 있다. 즉, 착수 모델은 이러한 과정을 반복하여 착수 신경망(330)이 대국에서 이기기 위한 더 좋은 착수점을 찾도록 트레이닝 될 수 있다. 일 예로, 착수 모델은 착수 손실(l)을 이용할 수 있다. 착수 손실(l)은 수학식 1과 같다.The starting model may be learned as a starting model to win a game by using the search unit 310, the self-play unit 320, and the starting neural network 330. More specifically, the search unit 310 may perform a Monte Carlo Tree Search (MCTS) operation according to the guidance of the initiating neural network 330 . MCTS is a heuristic search algorithm for some kind of decision making. That is, the search unit 310 may perform MCTS based on the movement probability value (p) and/or value value (V) provided by the initiating neural network 330 . For example, the search unit 310 guided by the initiating neural network 330 performs MCTS to search probability values that are probability distribution values for the initiation points (

) can be output. The self-play unit 320 provides a search probability value (

), you can play Go yourself. The self-play unit 320 proceeds to play Go by itself until the game is decided, and when the self-playing game ends, the checkerboard state (S), search probability value (

), the self-play value value z may be provided to the initiating neural network 330 . The checkerboard state (S) is a state in which Go stones are placed at starting points. The self-play value value (z) is a win rate value when the player plays a game in the checkerboard state (S). The onset neural network 330 may output a movement probability value (p) and a value value (V). The moving probability value (p) is a probability distribution value numerically indicating which starting point is a good number to win the game with respect to the starting points according to the checkerboard state (S). The value value (V) represents the winning rate when starting at the starting point. For example, a starting point having a high movement probability value p may be a good number. The onset neural network 330 determines that the movement probability value (p) is the search probability value (

), and the value (V) may be trained to be equal to the self-play value (z). Then, the trained initiating neural network 330 guides the search unit 310, and the search unit 310 uses the previous search probability value (

) to find a better number than the new search probability value (

) to output. For example, the start preparation time may follow any one of the start preparation time, the first start preparation time, and the second start preparation time according to the MCTS progress time. The start preparation time may be provided by the time management model server 500 and may be basically set to the average start preparation time. The self-play unit 320 provides a new search probability value (

) Based on the checkerboard state (S), a new self-play value value (z) is output, and the checkerboard state (S), a new search probability value (

), a new self-play value value z may be provided to the initiating neural network 330. The starting neural network 330 has a new search probability value (p) and value value (V).

) and a new self play value (z) can be trained again. That is, the starting model can be trained to find a better starting point for winning the game by repeating this process so that the starting neural network 330 can win. As an example, the launch model may use the launch loss (l). The landing loss (l) is shown in Equation 1.

(Equation 1)

는 신경망의 파라미터이고, c는 매우 작은 상수이다.

is a parameter of the neural network, and c is a very small constant.

와 p가 같아 지도록 하는 것은 크로스 엔트로피 손실(cross entropy loss) 텀에 해당되고,

에 c를 곱하는 것은 정규화 텀으로 오버피팅(overfitting)을 방지하기 위한 것이다.Making z and v equal in the starting loss (l) of Equation 1 corresponds to the mean square loss term,

and p are equal to the cross entropy loss term,

Multiplying by c is to prevent overfitting with the regularization term.

)은 하나의 착수점에서 위에 표시된 값으로 나타낼 수 있다. 탐색 확률값(

)은 착수 후보수의 방문 횟수를 전체 횟수로 나눈 비율일 수 있다. 일 예로, MCTS 시뮬레이션 전체 횟수가 1000번이고 90.00이라고 표시되어 있으면 해당 착수 후보수에 1000번 중 900번 방문했다는 것을 의미한다. 트레이닝 된 착수 모델의 가치값(V)은 도 4의 하나의 착수점에서 아래에 표시된 값으로 나타낼 수 있다. 착수 신경망(330)은 신경망 구조로 구성될 수 있다. 일 예로, 착수 신경망(330)은 한 개의 컨볼루션(convolution) 블록과 19개의 레지듀얼(residual) 블록으로 구성될 수 있다. 컨볼루션 블록은 3X3 컨볼루션 레이어가 여러개 중첩된 형태일 있다. 하나의 레지듀얼 블록은 3X3 컨볼루션 레이어가 여러개 중첩되고 스킵 커넥션을 포함한 형태일 수 있다. 스킵 커넥션은 소정의 레이어의 입력이 해당 레이어의 출력값과 합하여서 출력되어 다른 레이어에 입력되는 구조이다. 또한, 착수 신경망(330)의 입력은 흑 플레이어의 최근 8 수에 대한 돌의 위치 정보과 백 플레이어의 최근 8 수에 대한 돌의 위치 정보와 현재 플레이어가 흑인지 백인지에 대한 차례 정보를 포함한 19*19*17의 RGB 이미지가 입력될 수 있다.For example, referring to FIG. 3 , the trained initiation model may represent movement probability values p at the initiation points as probability distribution values as shown in FIG. 3 . Referring to FIG. 4, the search probability value of the trained initiation model (

) can be expressed as the value shown above at one starting point. Search probability value (

) may be a ratio of the number of visits of the starting candidates to the total number of visits. For example, if the total number of MCTS simulations is 1000 and 90.00 is displayed, it means that 900 out of 1000 visits were made to the number of starting candidates. The value (V) of the trained initiation model can be represented by the value shown below at one initiation point in FIG. 4 . The initiating neural network 330 may be composed of a neural network structure. For example, the initiating neural network 330 may include one convolution block and 19 residual blocks. A convolution block may have a form in which several 3X3 convolution layers are overlapped. One residual block may have a form in which several 3X3 convolutional layers are overlapped and a skip connection is included. A skip connection is a structure in which an input of a predetermined layer is outputted by summing an output value of a corresponding layer and inputted to another layer. In addition, the input of the starting neural network 330 is 19 * including stone position information for the last 8 moves of the black player, stone position information for the last 8 moves of the white player, and turn information on whether the current player is black or white. A 19*17 RGB image may be input.

)을 출력할 수 있다. 착수 모델은 착수점들 중 가장 높은 탐색 확률값(

)을 선택하여 착수할 수 있다. Referring to FIG. 5 , the learned start model may start using the start neural network 330 and the search unit 310 in its own turn. Through the selection process (a), the starting model is the second checkerboard state (S1) having a starting point with a high activity function (Q) and high confidence value (U) among the branches not searched through MCTS in the current first checkerboard state (S1). -2) is selected. The activity function (Q) is the average value of value values (V) calculated every time a corresponding branch is passed. The confidence value (U) is inversely proportional to the number of visits (N) passing through the corresponding branch and proportional to the movement probability value (p). The starting model may be expanded to the third checkerboard state (S1-2-1) at the selected starting point through the expansion and evaluation process (b) and calculate a movement probability value (p). The initiation model calculates the value value (V) of the extended third checkerboard state (S1-2-1), and the activity function (Q), number of visits (N), and movement probability value of the branches passed through the backup process (c) (p) can be stored. The initiation model repeats the process of selection (a), expansion and evaluation (b), and backup (c) during the preparation time for initiation, and a probability distribution is created using the number of visits (N) for each initiation point, and the search probability value (

) can be output. The initiation model has the highest search probability value among the initiation points (

) can be selected.

<Scenario Judgment Model>

6 is an exemplary diagram showing a screen for providing a layout judgment function of a deep learning-based Go game service according to an embodiment of the present invention, and FIG. 7 is for explaining the layout judgment model structure of the layout judgment model server of the present invention. FIG. 8 is a diagram for explaining one block of a neural network structure composed of a plurality of blocks of the situation judgment model of the present invention.

Referring to FIG. 6 , the deep learning-based Go game service according to an embodiment of the present invention can determine the situation of the current Go board state. For example, as shown in FIG. 6 , when a user clicks a situation judgment menu (A) during a game of Go on the screen of the terminal 100 and requests a situation judgment, the deep learning-based Go game service provides the situation judgment result in a pop-up window. can The situation judgment is to determine who is winning by how many points by calculating the opponent and my house during the game of Go. For example, if the user judges that the situation is advantageous to me, he will not overdo it anymore and will set up a strategy in the direction of ending the match while maintaining the current favorable situation as it is, and if it is judged that the situation is unfavorable, the game phase will be changed to a new one. There are several strategies you can try to do this. The criteria for judging the situation are house, private stone, stone, gongbae, and big according to the state in which the stones are placed on the board. A stone is a stone placed on a checkerboard and is not a score in Korean rules. A house is an area consisting of empty dots surrounded by stones of one color, which is a score in Korean rules. Gongbae and Big are areas that are neither black nor white when the game of Baduk is over, and are not points in Korean rules. Panwisa-seok is a stone that has no choice but to be caught among the stones placed on the checkerboard, and it is used to fill the opponent's house in Korean rules, so it is a score. Big refers to the area that is neither black nor white when the game of Go is over. Therefore, the judgment of the situation can be an accurate judgment only when accurately distinguishing or predicting the house, four stones, stones, gongbae, and big in the state of the checkerboard where the baduk stones are placed. At this time, accurately distinguishing House, Four Stone, Stone, Gongbae, and Big is to distinguish the state in which the house, four stone, stone, Gongbae, and Big are completely formed, and accurately predicting House, Four Stone, Dol, Gongbae, and Big is , it may be to predict a state with a high probability of becoming a stone, a stone, a ball, or a big.

Referring to FIG. 7 , the situation judgment model according to an embodiment of the present invention is a deep learning model of the situation judgment model server 400, and includes a situation judgment neural network 410, an input feature extractor 420, and a correct answer label generator 430. ) may be included.

The situation judgment model may perform supervised learning so that the situation of the current checkerboard state can be determined using the situation judgment neural network 410 . More specifically, a training data set related to the checkerboard state (S) of the layout judgment model is created, and the layout judgment neural network 410 learns to determine the layout according to the current checkerboard state (S) using the generated training data set can make it The position judgment model server 400 may receive a plurality of notations from the Go server 200 . Each notation of a plurality of notations may include each checkerboard state (S) according to the starting order.

The input feature extraction unit 420 may extract an input feature (IF) from a plurality of checkerboard states (S) and provide the input feature (IF) to the shape judgment neural network 410 as input data for training. The input feature (IF) of the checkerboard state (S) includes stone position information for the black player's last 8 moves, stone position information for the white player's last 8 moves, and turn information on whether the current player is black or white. It may be a 19*19*18 RGB image. For example, the input feature extractor 420 may have a neural network structure and may include a kind of encoder.

)로 제공할 수 있다. 정답 레이블 생성부(430)의 정답 레이블 생성은 후술하는 도 9 내지 도 11의 설명을 따른다. 일 예로, 정답 레이블 생성부(430)는 신경망 구조의 롤아웃 또는 인코더를 포함할 수 있다.The correct answer label generation unit 430 generates a correct answer label (ground truth) through a preprocessing process with the current checkerboard state (S), and transfers the correct answer label to the position judgment neural network 410 as target data for training (

) can be provided. The generation of correct answer labels by the correct answer label generator 430 follows the description of FIGS. 9 to 11 to be described later. For example, the correct answer label generation unit 430 may include a rollout or an encoder of a neural network structure.

)로 한 트레이닝 데이터 셋을 이용하여 형세 판단 신경망(410)에서 생성된 출력 데이터(o)가 타겟 데이터(

)와 동일해지도록 형세 판단 신경망(420)을 충분히 학습할 수 있다. 일 예로, 형세 판단 모델은 형세 판단 손실(

)을 이용할 수 있다. 형세 판단 손실(

)은 평균 제곱 에러(mean square error)를 이용할 수 있다. 예를 들어, 형세 판단 손실(

)은 수학식 2와 같다.The position judgment model takes input features (IF) as input data and correct answer labels as target data (

), the output data (o) generated by the situation judgment neural network 410 using the training data set is the target data (

), the situation determination neural network 420 may be sufficiently learned. As an example, the situation judgment model is a situation judgment loss (

) can be used. loss of judgment (

) can use the mean square error. For example, loss of judgment (

) is the same as Equation 2.

(Equation 2)

는 현재 바둑판 상태(S)에서 정답 레이블에 따른 소정의 교차점(i)에 대한 형세값이다. 형세값에 대한 설명은 후술하는 도 11의 설명에 따른다.

는 현재 바둑판 상태(S)에서 소정의 교차점(i)을 형세 판단 신경망(410)에 입력하였을 때에 출력되는 출력 데이터이다. 형세 판단 모델은 형세 판단 손실(

)이 최소화되도록 경사 하강법(gradient-descent)과 역전파(backpropagation)을 이용하여 형세 판단 신경망(410) 내의 가중치와 바이어스 값들을 조절하여 형세 판단 신경망(410)를 학습시킬 수 있다.B is the total number of intersections on the checkerboard. In the checkerboard, 19 horizontal and 19 vertical lines intersect each other, and 361 intersections are arranged. It is not limited thereto, and when the checkerboard has 9 horizontal lines and 9 vertical lines, 81 intersection points may be arranged.

is a positional value for a predetermined intersection point (i) according to the correct answer label in the current checkerboard state (S). Description of the shape value follows the description of FIG. 11 to be described later.

is output data that is output when a predetermined intersection (i) is input to the situation judgment neural network 410 in the current checkerboard state (S). The layout judgment model is based on the layout loss (

) is minimized, the situation judgment neural network 410 may be trained by adjusting weights and bias values in the situation judgment neural network 410 using gradient-descent and backpropagation.

The situation judgment neural network 410 may have a neural network structure. For example, the position judgment neural network 420 may be composed of 19 residual blocks. Referring to FIG. 8, one residual block includes 256 3X3 convolution layers, batch normalization layers, Relu activation function layers, 256 3X3 convolution layers, batch normalization layers, skip connections, Relu activation function layers may be arranged in order. The batch normalization layer is intended to prevent covariate shift, a phenomenon in which the distribution of inputs of the current layer changes due to changes in parameters of the previous layer during learning. The skip connection prevents the performance of the neural network from decreasing even when the block layer becomes thicker, and increases overall neural network performance by making the block layer thicker. The skip connection may be input to a second Relu activation function layer by summing the first input data of the residual block with the output of the second batch normalization layer.

9 and 10 are views for explaining first and second preprocessing steps for generating correct answer labels used to learn the situation judgment model of the present invention, and FIG. 11 is for learning the situation judgment model of the present invention. It is a diagram for explaining the third pre-processing step for generating the correct answer label used for the first step.

The correct answer label generation unit 430 may generate correct answer labels used for learning so that the situation judgment neural network 410 can accurately determine the situation.

More specifically, the correct answer label generation unit 430 receives the checkerboard state (S) based on the input data as an input, performs the first preprocessing of ending in the current checkerboard state (S), and performs the first preprocessing state (P1). ) can be created. The first preprocessing, the ending, is a process of finishing the game by making a predetermined start so that the boundary of the house becomes clear before calculating the house. For example, referring to FIG. 9 , the correct answer label generation unit 430 generates the first preprocessing state P1 of FIG. can

The correct answer label generation unit 430 may generate a second preprocessing state P2 by performing a second preprocessing of removing stones disposed within the house boundary and unnecessary for house classification in the first preprocessing state P1. For example, a stone that is placed within the boundary of a house and is unnecessary for house division may be a rubble stone. It was explained earlier that Sa-seok was a stone that died because the opponent's stone was placed in the house and no matter how it was placed, it was inevitable to be caught. In addition, a stone placed within the house boundary and unnecessary for house division may be a stone of one's own placed in the house. For example, referring to FIG. 9 , the correct answer label generation unit 430 removes stones unnecessary for house classification in the first preprocessing state P1 of FIG. 9(b) to the second preprocessing state of FIG. (P2) can be created.

As another example, referring to FIG. 10 , the correct answer label generation unit 430 embarks on a red x as shown in FIG. can do. The correct answer label generation unit 430 removes the rubble stones indicated by the blue x in FIG. Pre-processing can be done.

The correct answer label generation unit 430 may perform a third preprocessing in the second preprocessing state P2 to change each intersection into a shape value (g, where g is an integer) displayed from -1 to +1. That is, in the third pre-processing, the correct answer label generation unit 430 changes the second pre-processing state P2, which is an image feature, into the third pre-processing state P3, which is a numerical feature. For example, in the second preprocessing state P2, if my stone is placed at the intersection, 0, if it is my home area, +1, if the opponent's stone is placed, 0, and if it is the other house area, -1. In this case, the situation determination neural network 410 may be trained to distinguish between a house, a stone, and a private stone when determining a situation. As another example, in the second preprocessing state P2, 0 if my stone is placed at the intersection point, +1 if it is my home area, 0 if the other stone is placed, -1 if the other stone is placed, and 0 if it is big or common. In another example, the situation determination neural network 410 may be trained to distinguish a big or a bad outcome when determining a situation. For example, referring to FIG. 11, the correct answer label generator 430 characterizes the second preprocessing state P2 of FIG. 11(a) as the third preprocessing state P3 of FIG. 11(b). can be changed

)로 이용될 수 있다. The third preprocessing state (P3) becomes the correct answer label of the situation judgment in the checkerboard state (S), and the target data (

) can be used.

12 is a diagram for explaining the result of the situation judgment of the situation judgment model of the present invention.

When a checkerboard state is input, the learned layout judgment model may provide layout values for all intersections of the checkerboard. That is, an integer value of -1 to +1, which is a shape value, can be provided for 361 points of the checkerboard intersection.

Referring to FIG. 12 , the situation judgment model server 400 may determine the situation using the situation value provided by the situation judgment model, a predetermined threshold value, and the presence or absence of stones. For example, the layout judgment model server 400 may determine a place where there is no stone, and if the value of the layout exceeds a first threshold value, it is highly likely to be my home, and if the value is close to +1, it is determined to be my home area. there is. The layout judgment model server 400 may display a square of the same color as my stone, which gradually increases as the probability of being my house increases. The layout judgment model server 400 may determine that there is no stone, and if the value of the layout is less than the second threshold, the location is highly likely to be the other's house. The layout judgment model server 400 may display a square of the same color as the opponent's stone, which gradually increases as the probability of the opponent's house increases. The position judgment model server 400 is a place where there is no stone, and if the position value is within the range of the third threshold or a value close to 0, it can be judged as an empty score or a big match. The situation judgment model server 400 may indicate an X when it is judged as a common or a big match. The position judgment model server 400 is a place where there is a stone, and if the position value is within the third threshold range or a value close to 0, it can be determined as my stone or the opponent's stone. The situation judgment model server 400 may not display anything when it is determined that the situation judgment model server 400 is an empty match or a big match. The situation judgment model server 400 is a place where there is a stone, and if the value of the situation exceeds the first threshold, it is determined that it is likely to be the opponent's stone, and if the value is close to +1, it can be determined as the opponent's stone. there is. The layout judgment model server 400 may display a square of the same color as my stone, which gradually increases as the probability of the opponent's stone being rubbish increases. The situation judgment model server 400 is a place where there is a stone, and if the value of the situation is less than the second threshold, it is determined that it is a place with a high possibility of being a stone of my stone, and if it is close to -1, it can be determined as a stone of the opponent's stone. there is. The layout judgment model server 400 may display a square of the same color as the opponent's stone, which gradually increases as the probability of the opponent's stone being a rubble stone increases.

In addition, the situation judgment model server 300 may display the result of the count in the current checkerboard state by using the situation judgment criterion determined at each intersection.

In addition, the situation judgment model server 300 may generate change amount information and common multiple information of catchments according to the checkerboard state. For example, the situation determination model server 300 may generate information on the amount of change in the catchment by using the result of determining the situation in the checkerboard state according to the previous start and the result of the determination of the situation in the current checkerboard state. In addition, the position determination model server 300 may generate common multiple information using the position determination result in a checkerboard state.

Therefore, the device of the deep learning-based Go game service according to the embodiment may determine the situation of Go using the deep learning neural network. In addition, the deep learning-based Go game service device according to the embodiment can accurately determine the situation of Go by accurately classifying house, sandstone, stone, ball game, and big according to the rules of Go. In addition, the deep learning-based Go game service device according to the embodiment can accurately determine the situation of Go by predicting House, Four Stone, Stone, Gongbae, and Big according to the rules of Go. In addition, the deep learning-based Go game service device according to the embodiment can quickly determine the situation during the game of Go.

13 is a comparison between the situation judgment result of the situation judgment model of the present invention and the situation judgment result by the deep learning model according to the prior art, and FIG. 14 is the situation judgment result of the situation judgment model of the present invention and the situation judgment result according to the prior art 15 is a comparison of the situation judgment result by the deep learning model, and FIG. 15 is a comparison between the situation judgment result of the situation judgment model of the present invention and the situation judgment result of the deep learning model according to the prior art.

Referring to FIG. 13, the situation judgment model of the present invention determines the situation by classifying houses, stones, and rubble stones at each intersection point as in region B of FIG. 13(a). However, the situation judgment model using the deep learning model according to the prior art cannot distinguish between a house, a stone, and a sandstone at the intersection of the region corresponding to FIG. 13(a) in FIG. 13(b).

Likewise, referring to FIG. 14, the situation judgment model of the present invention determines the situation by classifying houses, stones, and rubble stones at each intersection point as in area C of FIG. 14 (a). However, the situation judgment model using the deep learning model according to the prior art cannot distinguish between a house, a stone, and a sandstone at the intersection of the region corresponding to FIG. 14(b) to FIG. 13(a).

Referring to FIG. 15 , the position judgment model of the present invention correctly recognizes a bag house as shown in area D of FIG. 15 (a). However, the situation judgment model based on the deep learning model according to the prior art cannot discriminate the back house in the area corresponding to FIG. 15(a) in FIG. 15(b).

16 is an exemplary diagram of a signal flow in a deep learning-based Go game service system according to an embodiment of the present invention.

Referring to FIG. 16, the start model server 300 is an artificial intelligence computer that learns by itself according to the rules of Go and trains the start model, which is a deep learning model, so that it can perform the start of the Go stone so that it can win the game in its own turn. can (s11). The Go server 22 may transmit a plurality of notations to the situation judgment model server 400 . The situation judgment model server 400 may generate a training data set. First, the position judgment model server 400 may extract input features from a checkerboard state of a plurality of notations (S13). The position judgment model server 400 may generate a correct answer label using the checkerboard state from which the input features are extracted (S14). The layout judgment model server 400 may train a layout judgment model using a training data set in which input features are input data and correct answer labels are target data (S15). The terminal 100 may request the Go server 200 to play a Go game against an artificial intelligence computer or against another user terminal (S16). The Go server 200 may request the start model server 300 to start when the terminal 100 requests a Go game against the artificial intelligence computer (S17). The Go server 200 proceeds with the Go game, and the terminal 100 and the start model server 300 may perform a start on their turn (S18 to S20). During the game, the terminal 100 may request the Go server 200 to determine the situation (S21). The Go server 200 may request the position judgment model server 400 to determine the position of the current Go board state (S22). The layout judgment model server 400 extracts the input features of the current checkerboard state, the layout judgment model, which is a deep learning model, generates a layout value using the input features, and performs the layout judgment using the checkerboard status and the layout values. It can (S23). The situation judgment model server 400 may provide the situation judgment result to the Go server 200 (S24). The Go server 200 may provide the result of the situation determination to the terminal 100 (S25).

17 is a method for determining a situation among deep learning-based Go game service methods according to an embodiment of the present invention, and FIG. 18 is a method for preprocessing training data to generate a correct answer label among the method for determining a situation in FIG. 17 .

Referring to FIG. 17, the deep learning-based Go game service method according to an embodiment of the present invention may include a step (S100) of receiving a plurality of notations from the Go server by the situation judgment model server.

The deep learning-based Go game service method according to an embodiment of the present invention may include a step (S200) of extracting input features from a plurality of notation checkerboard states by an input feature extraction unit in a layout judgment model of a layout judgment model server. . A method of extracting input features follows the description of FIG. 7 .

The deep learning-based Go game service method according to an embodiment of the present invention may include a step of generating a correct answer label based on a checkerboard state from which an input feature is extracted by a correct answer label generator in a situation judgment model (S300). For example, referring to FIG. 18 , the correct answer label generating step ( S300 ) may include a first preprocessing step ( S301 ) in which the correct answer label generating unit finishes in the current checkerboard state. The first preprocessing step (S301) follows the description of FIGS. 9 to 10. The correct answer label generation step (S300) may include a second preprocessing step (S302) of removing unnecessary stones from the first preprocessed checkerboard state by the correct answer label generator. The second pre-processing step (S302) follows the description of FIGS. 9 to 10. The correct answer label generating step (S300) may include a third preprocessing step (S303) of changing each intersection of the second preprocessed checkerboard state into a shape value by the correct answer label generating unit. The third pre-processing step (S303) follows the description of FIG. The correct answer label generation step (S300) may include a step (S303) of providing the third preprocessing state as the correct answer label to the situation judgment neural network as target data. The step of providing target data (S301) follows the description of FIGS. 7 and 11.

The deep learning-based Go game service method according to an embodiment of the present invention may include training a situation judgment neural network of a situation judgment model using a training data set (S400). A method of training (learning) the situation decision neural network follows the description of FIG. 7 .

The deep learning-based Go game service method according to an embodiment of the present invention includes a step (S500) of constructing a situation judgment model by completing training of a situation judgment neural network. For example, the training of the situation judgment neural network may be completed when the situation judgment loss of FIG. 7 becomes less than or equal to a predetermined value.

The deep learning-based Go game service method according to an embodiment of the present invention may include a step (S600) of inputting a current checkerboard state to a situation judgment model in response to a situation judgment request from a terminal.

The deep learning-based Go game service method according to an embodiment of the present invention may include a step (S700) of determining the situation of the current Go board state, to which the situation judgment model is input. The step of determining the situation (S700) may follow the description that the situation judgment model described in FIG. 12 generates the situation value of the current checkerboard state.

The deep learning-based Go game service method according to an embodiment of the present invention may include a step (S800) of outputting a situation judgment result by a situation judgment model server. The step of outputting the situation judgment result (S800) may follow the description of providing the situation judgment result by using the situation value, the state of the checkerboard, and a predetermined threshold value by the situation judgment model server described in FIG. 12 .

Therefore, the deep learning-based Go game service method according to the embodiment may determine the situation of Go using a deep learning neural network. In addition, the deep learning-based Go game service method according to the embodiment can accurately determine the situation of Go by accurately classifying house, sandstone, stone, ball game, and big according to the rules of Go. In addition, the deep learning-based Go game service method according to the embodiment can accurately determine the situation of Go by predicting House, Four Stone, Stone, Gongbae, and Big according to the Go rules. In addition, the deep learning-based Go game service method according to the embodiment can quickly determine the situation during a game of Go.

<Time management model server according to an embodiment>

19 is a diagram for explaining a time management unit of a time management model server according to an embodiment of the present invention, and FIG. 20 is a diagram for explaining distributed calculation of a time management unit according to an embodiment of the present invention.

In the deep learning-based Go game service according to an embodiment of the present invention, the time management model server 500 determines the start preparation time, which is one of time management information, and the start model is set based on the preset or determined start preparation time. can get started The time management model server 500 increases the start preparation time to find a good move so that the artificial intelligence computer can make a better move when the game is close to the game between the user and the artificial intelligence computer or the artificial intelligence computer. can make it

), 방문 횟수(N) 또는 가치값(V)을 수신하고, 착수 모델 서버(300)로 착수 준비 시간(PP)을 제공할 수 있다. 일 예로, 착수 준비 시간(PP)은 평균 착수 준비 시간, 제1 착수 준비 시간 또는 제2 착수 준비 시간을 포함할 수 있다. 제1 착수 준비 시간은 평균 착수 준비 시간보다 길고, 제2 착수 준비 시간은 제1 착수 준비 시간보다 길 수 있다. 시간 관리 모델 서버(500)는 시간 관리부(510)를 포함할 수 있다. 시간 관리부(510)는 탐색 확률값(

) 및 방문 횟수(N)에 기초하여 착수 준비 시간(PP)을 결정할 수 있다. 시간 관리부(510)는 탐색 확률값(

), 방문 횟수(N)을 이용하여 분산을 산출하고, 분산과 임계 분산 값을 비교하여 착수 준비 시간을 결정할 수 있다. 즉, 착수 후보점에 대한 분산이 낮을수록 착수 후보점이 가장 좋은 수가 아닐 가능성이 높다는 것이고 이러한 정보에 비추어 현재 경기가 막상막하일 가능성이 높을 수 있다. 이에, 시간 관리부는 분산이 임계 분산 값보다 낮을 경우 착수 준비 시간을 더 길게 하여 착수 모델이 더 좋은 수를 착도록 할 수 있다. 분산은 수학식 3과 수학식 4를 이용하여 구할 수 있다.More specifically, referring to FIG. 19 , the time management model server 500 obtains a search probability value from the initiation model server 300 (

), the number of visits (N) or value (V), and the start preparation time (PP) may be provided to the start model server 300. For example, the start preparation time (PP) may include an average start preparation time, a first start preparation time, or a second start preparation time. The first start preparation time may be longer than the average start preparation time, and the second start preparation time may be longer than the first start preparation time. The time management model server 500 may include a time management unit 510 . The time management unit 510 determines the search probability value (

) and the number of visits (N), the start preparation time (PP) can be determined. The time management unit 510 determines the search probability value (

), the number of visits (N) is used to calculate the variance, and the start preparation time can be determined by comparing the variance and the critical variance value. That is, the lower the variance of the starting candidate points, the higher the possibility that the starting candidate point is not the best number. Accordingly, when the variance is lower than the critical variance value, the time management unit may make the start preparation time longer so that the start model arrives at a better number. The variance can be obtained using Equations 3 and 4.

(Equation 3)

는 각 교차점에 대한 방문 횟수이고,

는 각 교차점에 대한 탐색 확률값이고,

는 평균 방문 횟수이다.In Equation 3, n is the number of intersection points,

is the number of visits to each intersection,

is the search probability for each intersection,

is the average number of visits.

(Equation 4)

In Equation 4, Var is the variance.

The time management unit 510 may determine the start preparation time as the first start preparation time when the variance (Var) is lower than the critical variance value, and the start preparation time as the average start preparation time when the variance (Var) is not lower than the critical variance value. can determine

For example, referring to FIG. 20 , the critical variance value may be 0.05. In the case of FIG. 20 (a), the time management unit 510 may calculate the variance (Var) as 0.2109. In this case, the time manager 510 may determine the start preparation time as the average start preparation time since the variance Var is higher than the critical variance value. In the case of FIG. 20 (b), the time management unit 510 may calculate the variance (Var) as 0.0145. In this case, the time manager 510 may determine the start preparation time as the first start preparation time since the variance (Var) is lower than the critical variance value. Accordingly, the initiation model may select a better initiation candidate point by performing MCTS simulations for a longer time or a greater number of times.

In addition, the time management unit 510 may determine the start preparation time (PP) based on the value (V). More specifically, the time management unit 510 may determine the start preparation time PP as the second start preparation time when the value V is less than or equal to the threshold value after determining the first start preparation time. For example, the threshold value may be 50.0%. That is, since the value of the starting candidate point is low, it is likely that the current game is lost, so the starting model needs to perform MCTS simulations for a longer time or a greater number of times so that the time management unit 510 can find a better starting point. is to make it possible

Therefore, the deep learning-based Go game service device according to the embodiment can manage the Go game time. In addition, the deep learning-based Go game service device according to the embodiment may change the starting preparation time in an important phase.

21 is an exemplary diagram of a signal flow in a Go game service system of a time management model server according to an embodiment of the present invention.

), 방문 횟수(N), 가치값(V)를 시간 관리 모델 서버(500)에 전송할 수 있다(S2102). 시간 관리 모델 서버(500)는 수신한 탐색 확률값(

), 방문 횟수(N), 가치값(V)에 기초하여 착수 준비 시간을 결정할 수 있다(S2103). 시간 관리 모델 서버(500)는 결정된 착수 준비 시간을 착수 모델 서버(300)에 전송할 수 있다(S2104). 착수 모델 서버(300)는 수신한 착수 준비 시간 또는 기설정된 착수 준비 시간에 기초하여 착수를 수행할 수 있다(S2105). 착수 모델 서버(300)는 수신한 착수 준비 시간과 기설정된 착수 준비 시간 중 수신한 착수 준비 시간에 우선하여 착수를 수행할 수 있다. Referring to FIG. 21 , the start model server 300 may perform MCTS simulation to find a starting point for winning a game among players (S2101). The initiation model server 300 is a search probability value generated as a result of the MCTS simulation (

), the number of visits (N), and the value (V) may be transmitted to the time management model server 500 (S2102). The time management model server 500 receives the search probability value (

), the number of visits (N), and the value (V), the start preparation time may be determined (S2103). The time management model server 500 may transmit the determined start preparation time to the start model server 300 (S2104). The start model server 300 may perform the start based on the received start preparation time or the preset start preparation time (S2105). The start model server 300 may perform the start in priority to the received start preparation time of the received start preparation time and the preset start preparation time.

22 is a method for determining start preparation time among deep learning-based Go game service methods according to an embodiment of the present invention.

), 방문 횟수(N) 또는 가치값(V)을 수신하는 단계(S2201)을 포함할 수 있다. Referring to FIG. 22, in the deep learning-based Go game service method according to an embodiment of the present invention, the time management model server 500 has a search probability value (

), receiving the number of visits (N) or value (V) (S2201).

), 방문 횟수(N)에 기초하여 분산을 산출하는 단계(S2202)를 포함할 수 있다. 분산을 산출하는 방법은 도 19 및 도 20의 설명을 따른다.In the deep learning-based Go game service method according to an embodiment of the present invention, the time management unit of the time management model server 500 sets the time management model server 500 to a search probability value (

), calculating a variance based on the number of visits (N) (S2202). The method for calculating the variance follows the description of FIGS. 19 and 20 .

The deep learning-based Go game service method according to an embodiment of the present invention may include determining whether the variance calculated by the time management unit of the time management model server 500 is less than a critical variance value (S2203).

In the deep learning-based Go game service method according to an embodiment of the present invention, the time management unit of the time management model server 500 determines the start preparation time as the average start preparation time if the variance is not less than the critical variance value (S2204 ) may be included. The method of determining the start preparation time as the average start preparation time follows the description of FIGS. 19 and 20 .

In the deep learning-based Go game service method according to an embodiment of the present invention, the time management unit of the time management model server 500 determines the start preparation time as the first start preparation time when the variance is less than the critical variance value (S2205 ) may be included. A method of determining the start preparation time as the first start preparation time follows the description of FIGS. 19 and 20 .

In the deep learning-based Go game service method according to an embodiment of the present invention, the time management unit of the time management model server 500 determines the start preparation time as the first start preparation time and determines whether the value is less than or equal to the threshold value Step S2206 may be included. A method of determining whether the value is less than or equal to the threshold value follows the description of FIGS. 19 and 20 . The time management unit may determine the first start preparation time as the start preparation time when the value is not less than or equal to the threshold value.

In the deep learning-based Go game service method according to an embodiment of the present invention, the time management unit of the time management model server 500 determines the start preparation time as the second start preparation time when the value is less than the threshold value ( S2207) may be included. A method of determining the start preparation time as the second start preparation time follows the description of FIGS. 19 and 20 .

The deep learning-based Go game service method according to an embodiment of the present invention may include transmitting the start preparation time determined by the time management unit of the time management model server 500 (S2208).

Therefore, the deep learning-based Go game service method according to an embodiment can manage the Go game time. In addition, the deep learning-based Go game service method according to an embodiment may change the starting preparation time in an important phase.

<Time management model server according to another embodiment>

23 is a diagram for explaining a time adjustment unit of a time management model server according to another embodiment of the present invention, and FIG. 24 is a method for determining start preparation time among deep learning-based Go game service methods according to another embodiment of the present invention. 25 is an example of a diagram for explaining a method of determining a start preparation time according to a trend of change in value according to another embodiment of the present invention, and FIG. 26 is an example of a value according to another embodiment of the present invention. It is an example of a drawing for explaining a method of determining the start preparation time according to the fluctuation range.

23, in the deep learning-based Go game service according to another embodiment of the present invention, when the time management model server 500 determines the start preparation time (PP), which is one of the time management information, the value value (V ) and/or the relative start time (OT: that is, the other party's start preparation time) to determine the start preparation time (PP). In addition, the deep learning-based Go game service according to another embodiment may start based on a preset or determined starting preparation time (PP).

At this time, the time management model server 500 is a better way for the artificial intelligence computer to start when an important phase of the game is faced in a match between the user and the artificial intelligence computer or a match between the artificial intelligence computer (eg, when the match is close). You can increase the set-up preparation time (PP) to find a good move to make a move.

In detail, the time management model server 500 may include a time adjustment unit 550 . The time adjustment unit 550 may determine the start preparation time (PP) based on the value (V) and/or the relative start time (OT).

Specifically, referring to FIG. 24, in the deep learning-based Go game service method according to another embodiment of the present invention, the time management model server 500 determines the start preparation time (PP) based on the value (V). can do

In detail, in the deep learning-based Go game service method according to another embodiment of the present invention, the time management model server 500 determines the starting preparation time (PP) according to the change in the value value (V), that is, the change in the odds ratio. can do.

In more detail, the deep learning-based Go game service method according to another embodiment of the present invention may include receiving a plurality of value values (V) by the time management model server 500 (S2301).

That is, the time management model server 500 may receive a plurality of value values V according to each start of the Go game from the start model server 300 .

In addition, the deep learning-based Go game service method according to another embodiment of the present invention may include accumulating and storing a plurality of received value values (V) by the time management model server 500 (S2302). .

In detail, the time management model server 500 may sequentially accumulate and store a plurality of value values V received from the undertaking model server 300 .

In addition, the deep learning-based Go game service method according to another embodiment of the present invention may include a step of determining, by the time management model server 500, a change in odds based on the accumulated value (V) (S2303). there is.

Specifically, the time management model server 500 may grasp a trend and/or a range of change of the value value V based on the accumulated value value V.

Here, the change rate of the value V may mean a rate of change of the value V indicating whether the value V (ie, win rate) is gradually increasing or decreasing. In detail, the change trend of the value value (V) represents the change state of a plurality of value values (V) from a predetermined start time to the current start time after the start of the game. More specifically, when the rate of change for each of the plurality of value values (V) is a negative number more than a predetermined number of times, that is, if the value value (V) decreases more than a predetermined number of times, the value value (V) gradually decreases. It can be judged that the situation is doing. On the other hand, when the rate of change for each of the plurality of value values (V) is a positive number more than a predetermined number of times, that is, if the value value (V) increases more than a predetermined number of times, the value value (V) is gradually increasing. can be judged. In addition, the change trend of the value value (V) may be determined by the slope of the change rate of the corresponding value value (V).

For example, the trend of change of the value value (V) is the change rate and/or the slope (ie, whether the value value (V) is rising or the value value (V) for the recently launched number 5). is falling).

On the other hand, the variation range of the value value (V) may mean the amount of change (degree of change) of the value value (V).

In addition, the time management model server 500 may determine a change in odds ratio based on the plurality of accumulated values V based on a trend and/or range of change of the identified value V.

Continuing, in the deep learning-based Go game service method according to another embodiment of the present invention, the time management model server 500 determines the starting preparation time (PP) according to the change in the odds ratio determined as above (S2304). can include

In detail, referring to FIG. 25 , the time management model server 500 may determine the set-up preparation time (PP) according to the change trend of the value value (V).

In more detail, the time management model server 500 may increase the set-up preparation time (PP) when the trend of change based on the plurality of value values (V) continuously decreases. This is in consideration of the fact that it is an important phase in which more preparation time (PP) must be allocated to find a better starting point if the winning rate continues to drop.

For example, in the time management model server 500, the change trend of the value V for the recently launched 5 numbers (eg, 72 numbers, 74 numbers, 76 numbers, 78 numbers, and 80 numbers) gradually decreases. (e.g., the value of the number 72: 70%, the value of the number 74: 67%, the value of the number 76: 65%, the value of the number 78: 63%, and the value of the number 80: 61%) , it is judged to be an important phase, and the launch preparation time (PP) to be used now can be increased.

In addition, referring to FIG. 26 , the time management model server 500 may determine the set-up preparation time (PP) according to the variation range of the value (V).

In detail, the time management model server 500 determines that the situation is an important phase when the fluctuation range of the value value V is greatly reduced by more than a predetermined criterion (eg, a predetermined value and/or ratio). In order to find a better starting point, more starting preparation time (PP) can be allocated.

For example, the time management model server 500 determines the variation range of the value (V) between two recently initiated numbers (eg, 64 and 66) based on a predetermined criterion (eg, a preset value and/or ratio). In the case of a situation where the value of 64 numbers is greatly reduced (eg, the value of 64 numbers: 58% and the value of 66 numbers: 35%), it is determined that it is an important phase and the start preparation time (PP) to be used at present can be increased.

At this time, when the time management model server 500 increases the start preparation time (PP), the start preparation time (PP) increase amount (hereinafter, start time increase amount) for how much to increase the start preparation time (PP) is set to a predetermined amount. It can be decided according to the method.

In detail, the time management model server 500 may set the start preparation time (PP) determined by the above-described time management unit 510 as the basic start preparation time (hereinafter, the basic start time).

In addition, the time management model server 500 may determine the start time increment based on 1) the basic start time.

Specifically, the time management model server 500 may determine the start time increment by a predetermined value calculated based on a predetermined operation based on the basic start time.

In an embodiment, the time management model server 500 may determine the start time increase amount to increase by a predetermined percentage (eg, a percentage less than or equal to a predetermined value) compared to the basic start time. For example, if the basic start time is 10 seconds and the predetermined percentage is 30%, the time management model server 500 may determine the start time increment as 3 seconds.

In addition, the time management model server 500 may determine the adjusted start preparation time (PP) by reflecting the determined start time increment to the basic start time.

Alternatively, the time management model server 500 may 2) determine the starting time increment based on a preset countdown time on the Go game service.

In detail, the time management model server 500 determines the countdown time preset for the Go game service as the start time increment, and adds it to the basic start time determined by the time management unit 510 to adjust the start preparation time (PP) ) can be determined. For example, if the preset countdown time is 60 seconds and the basic start time is 10 seconds, the time management model server 500 may determine the finally adjusted start preparation time (PP) as 70 seconds.

Subsequently, the time management model server 500 may provide the start preparation time (PP) determined as above to the start model server 300 .

That is, the time adjustment unit 550 of the time management model server 500 performs MCTS simulations for a longer time or a greater number of times so that the initiation model of the initiation model server 300 having received the initiation preparation time (PP) is better. It is possible to select a starting point.

Therefore, the deep learning-based Go game service device according to the embodiment can manage the Go game time. In addition, the deep learning-based Go game service device according to the embodiment may change the starting preparation time (PP) in an important phase.

27 is a method for determining start preparation time (PP) among deep learning-based Go game service methods according to another embodiment of the present invention, and FIG. 28 is a start according to relative start time (OT) according to another embodiment of the present invention. It is an example of a drawing for explaining a method of determining the preparation time (PP).

Meanwhile, referring to FIG. 27, in the deep learning-based Go game service method according to another embodiment of the present invention, the time management model server 500 determines the start preparation time (PP) based on the relative start time (OT). can do

In detail, in the deep learning-based Go game service method according to another embodiment of the present invention, the time management model server 500 takes the opponent's start immediately before the opponent's start (ie, the opponent's start that started right before the current point in time) The start preparation time (PP) can be determined using the time (hereinafter, the previous relative start time) and the average of the plurality of relative start times (OT) used by the other party (hereinafter, the average relative start time).

Specifically, the deep learning-based Go game service method according to another embodiment of the present invention may include a step of receiving, by the time management model server 500, a plurality of relative start times (OT) (S2401).

In more detail, the time management model server 500 is the start preparation time of the other party (ie, opponent A plurality of start times (OT) may be received.

At this time, the received plurality of relative start times (OT) may include all relative start times (OT) for the time from a predetermined time after the game starts to the point before the previous start time or the previous start time excluding the previous start time. there is.

For example, the plurality of relative launch times (OT) include all relative launch times (OT) from the first relative launch time (OT) for the first launch at the time of the other party's first launch to the previous relative launch time. Or it may include all relative start times (OT) from the first relative start time (OT) for the first start at the time of the other party's first start to the previous relative start time excluding the immediately preceding relative start time.

In addition, in the deep learning-based Go game service method according to another embodiment of the present invention, the time management model server 500 calculates an average relative start time according to the received plurality of relative start times (OT) (S2402 ) may be included.

As an embodiment, the time management model server 500 may calculate the average relative start time by performing an average operation on the plurality of relative start times (OT).

In addition, in the deep learning-based Go game service method according to another embodiment of the present invention, the time management model server 500 calculates the start preparation time (PP) based on the average relative start time and the previous relative start time calculated as above. A step of determining (S2403) may be included.

At this time, the previous relative start time may be included in the plurality of relative start times (OT) and received from the start model server 300 and/or the Go server 500, and the plurality of relative start times (OT) and may be separately received from the set-up model server 300 and/or the Go server 500.

In detail, referring to FIG. 28, the time management model server 500 determines that the previous relative start time required for the other party's previous start (ie, the other party's start that started right before the current point in time) is greater than the calculated average relative start time. If it is large, the launch preparation time (PP) can be increased. This is in consideration of the fact that the opponent's previous start was an important phase for the opponent that the opponent used more start preparation time (PP) in the previous start than the average start preparation time (PP) used in the game process. Therefore, the current start after the previous start, which was an important phase for the other party, is also an important phase, and the start preparation time (PP) can be increased to allocate more start preparation time (PP) to find a better starting point.

For example, the time management model server 500 performs each of 8 moves (eg, 54 moves, 56 moves, 58 moves, 60 moves, 62 moves, 64 moves, 66 moves and the previous 68 moves) that the other party recently started. A plurality of relative start times (OT) including the relative start time (OT) for (e.g., 54 number relative start time: 20 seconds, 56 number relative start time: 10 seconds, 58 number relative start time: 20 seconds, 60 number relative launch time: 10 seconds, 62 number relative launch time: 30 seconds, 64 number relative launch time: 5 seconds, 66 number relative launch time: 2 seconds, and 68 number relative launch time: 38 seconds).

Also, in this example, the time management model server 500 may calculate an average relative start time (eg, about 14 seconds) by performing an average operation on a plurality of received relative start times (OT).

In addition, in this example, the time management model server 500 compares the calculated average relative start time (eg, about 14 seconds) with the previous relative start time (eg, 38 seconds) for the previous start (eg, 68 moves). can

In addition, in this example, if the time management model server 500 determines that the previous relative start time (eg, 38 seconds) is greater than the average relative start time (eg, about 14 seconds) through the comparison, it is determined that it is an important phase. and increase the launch preparation time (PP) currently used.

In this case, when the start preparation time PP is increased, the time management model server 500 may determine the start time increase amount for how much to increase the start preparation time PP according to a predetermined method.

In detail, as described above, the time management model server 500 may 1) determine the start time increment based on the basic start time determined by the time management unit 510 .

Specifically, the time management model server 500 may determine the start time increment by a predetermined value calculated based on a predetermined operation based on the basic start time.

In an embodiment, the time management model server 500 may determine the start time increase amount to increase by a predetermined percentage (eg, a percentage less than or equal to a predetermined value) compared to the basic start time. For example, if the basic start time is 10 seconds and the predetermined percentage is 30%, the time management model server 500 may determine the start time increment as 3 seconds.

In addition, the time management model server 500 may determine the adjusted start preparation time (PP) by reflecting the determined start time increment to the basic start time.

Alternatively, the time management model server 500 may 2) determine the starting time increment based on a preset countdown time on the Go game service.

In detail, the time management model server 500 determines the countdown time preset for the Go game service as the start time increment, and adds it to the basic start time determined by the time management unit 510 to adjust the start preparation time (PP) ) can be determined. For example, if the preset countdown time is 60 seconds and the basic start time is 10 seconds, the time management model server 500 may determine the finally adjusted start preparation time (PP) as 70 seconds.

Alternatively, the time management model server 500 may determine the start time increment according to 3) the relative start time (OT).

In this case, the relative start time OT may include a previous relative start time and/or an average relative start time.

Specifically, the time management model server 500 compares the 1] basic start time with the previous relative start time, and determines the start time having the larger time as the start preparation time (PP) to be used for the current start. can

That is, when the previous relative start time is greater than the basic start time derived from the above-described time management unit 510, the time management model server 500 may determine the corresponding gap as the start time increment. For example, if the basic start time is 35 seconds and the previous relative start time is, for example, 38, the time management model server 500 sets the previous relative start time having a larger time as the start preparation time (PP). In order to do so, the increase in the start time may be determined as 3 seconds, which is the gap between the two start times.

Alternatively, the time management model server 500 may calculate the ratio of the previous relative start time to the average relative start time (hereinafter referred to as relative start time ratio) and determine the increment of start time based on the calculated relative start time ratio. there is.

In detail, the time management model server 500 calculates the relative start time ratio (ie, the previous relative start time/average relative start time) to determine the percentage of the previous start compared to the start preparation time (PP) used by the other party on average. of time can be judged.

In addition, the time management model server 500 determines whether the calculated relative start time ratio (eg, a predetermined percentage) meets a predetermined criterion (eg, exceeds a predetermined threshold value (eg, 1.00%), etc.) In this case, the start time increment may be determined by reflecting the relative start time ratio to the basic start time. That is, when the time management model server 500 determines that the counterpart has spent a larger amount of time on the previous start than the average start preparation time (PP) based on the relative start time ratio (for example, the relative start time ratio is set at a predetermined start time ratio). exceeds the threshold value of), the start time increment may be determined by applying the corresponding relative start time ratio to the basic start time.

In an embodiment, the time management model server 500 determines the relative start time ratio using the calculated relative start time ratio and the basic start time when the calculated relative start time ratio exceeds a predetermined threshold value (eg, 1.00%). It is possible to perform an operation (eg, a multiplication operation, etc.) of, and the start time increase amount can be determined based on the result of the operation. Here, the predetermined criterion is limited to exceeding a predetermined threshold value (eg, 1.00%), but is not limited thereto.

For example, the time management model server 500 calculates the average relative start time when the basic start time is '30 seconds', the average relative start time is '40 seconds', and the previous relative start time is '60 seconds'. And the relative start time ratio can be calculated as '1.5% (60 seconds / 40 seconds)' based on the previous relative start time. In addition, the time management model server 500 may determine whether the calculated relative start time ratio exceeds '1.00%', a predetermined threshold value. In addition, the time management model server 500 sets the relative start time ratio ('1.5%' in this example) to the basic start time (in this example, when the relative start time ratio exceeds a predetermined threshold). '30 seconds') can be applied using a predetermined operation (multiplication operation in this example). Thus, the time management model server 500 may derive the final set-up preparation time (PP) as '45 seconds ('30 seconds * 1.5')' and determine the set-up time increment accordingly as '15 seconds'.

That is, the time management model server 500 may determine the start time increase based on the relative start time (OT), which is the time required for the other party to start in the game process, and reflect the determined start time increase to the basic start time An adjusted launch preparation time (PP) can be determined.

In addition, the time management model server 500 may provide the start preparation time (PP) determined as above to the start model server 300 .

Thus, the time adjustment unit 550 of the time management model server 500 performs the MCTS simulation for a longer time or a greater number of times so that the initiation model of the initiation model server 300 having received the initiation preparation time (PP) improves the initiation preparation time (PP). candidate points can be selected.

Therefore, the deep learning-based Go game service device according to the embodiment can manage the Go game time and change the starting preparation time (PP) in an important phase.

<Time management model server according to another embodiment>

29 is a diagram for explaining a time management model of a time management model server according to another embodiment of the present invention, and FIG. 30 is a collection variation used to generate game time information according to another embodiment of the present invention. , FIG. 31 is a diagram for explaining a collection variation used to generate game time information according to another embodiment of the present invention, and FIG. 32 is a diagram for a game according to another embodiment of the present invention. It is a diagram for explaining common multiples used to generate time information.

Referring to FIG. 29 , the deep learning-based Go game service according to an embodiment of the present invention may generate game time information in the current Go board state by using the time management model of the time management model server 500. The game time information may include the remaining game length predicted until the end of the game according to the state of the checkerboard. The time management model is a deep learning model of the time management model server 500 and may include a first time management neural network 520 , a second time management neural network 530 , and a first input feature generator 540 .

The time management model may perform supervised learning to predict the remaining game length, which is game time information in a current checkerboard state. More specifically, the time management model server 500 generates a training data set for predicting the remaining game length according to the checkerboard state (S), and uses the created training data set to determine the time management model in the current checkerboard state (S). It can be trained to predict the length of the remaining game. The time management model server 500 may receive a plurality of notations from the Go server 200 . Each notation of a plurality of notations may include each checkerboard state (S) according to the starting order. In addition, each notation of the plurality of ups and downs may include game time information, in particular, remaining game length information until the end of the game in each checkerboard state (S). Also, the time management model server 500 may receive situation determination information from the situation determination model server 400 . The situation judgment information is situation judgment information based on a plurality of notations provided from the Go server 200 to the time management model server 500, and may include change amount information and common multiple information of collections. Also, the time management model server 500 may receive a value from the undertaking model server 300 . The value may be a value based on a plurality of notations provided by the Go server 200 to the time management model server 500.

The first input feature extractor 540 may extract the first input feature IF1 from the checkerboard state S of a plurality of notations and provide the first input feature IF1 to the time management first neural network 520 as input data for training. The first input feature (IF) of the checkerboard state (S) is the position information of the stone for the last 8 moves of the black player, the position information of the stone for the last 8 moves of the white player, and the turn information on whether the current player is black or white. It may be a 19*19*18 RGB image including . For example, the first input feature extractor 540 may have a neural network structure and may include a kind of encoder.

In addition, the time management model may provide the second input feature IF2 and the third input feature IF3 to the first time management neural network 520 as input data for training. The second input feature IF2 may be change amount information of the catchment according to the checkerboard state S. The third input feature IF3 may be common multiple information according to the checkerboard state S. The time management model may provide the fourth input feature IF4 to the second time management neural network 530 as input data for training. The fourth input feature IF4 may be a value according to the checkerboard state S.

The first time management neural network 520 may provide an output value to the second time management neural network 530 by using the first to third input features IF1 to IF3 as inputs. The first time management neural network 520 may have a neural network structure. For example, the first time management neural network 520 may include 20 residual blocks. Referring to FIG. 8, one residual block includes 256 3X3 convolution layers, batch normalization layers, Relu activation function layers, 256 3X3 convolution layers, batch normalization layers, skip connections, Relu activation function layers may be arranged in order. The batch normalization layer is intended to prevent covariate shift, a phenomenon in which the distribution of inputs of the current layer changes due to changes in parameters of the previous layer during learning. The skip connection prevents the performance of the neural network from decreasing even when the block layer becomes thicker, and increases overall neural network performance by making the block layer thicker. The skip connection may be input to a second Relu activation function layer by summing the first input data of the residual block with the output of the second batch normalization layer.

The second time management neural network 530 may generate game time information about the predicted remaining game length by taking the output value of the first time management neural network 520 and the fourth input feature IF4 as inputs. The time management second neural network 530 may have a neural network structure. For example, the second time management neural network 530 may have a fully connected layer structure.

)로 한 트레이닝 데이터 셋을 이용하여 시간 관리 제1 신경망(520)을 거쳐 시간 관리 제2 신경망(530)에서 생성된 출력 데이터(r)가 타겟 데이터(

)와 동일해지도록 시간 관리 제1 신경망(520) 및 시간 관리 제2 신경망(530)을 충분히 학습할 수 있다. 일 예로, 시간 관리 모델은 남은 경기 길이 예측 손실(

)을 이용하여 남은 경기 길이 예측 손실(

)이 최소가 되도록 트레이닝 할 수 있다. 예를 들어, 남은 경기 길이 예측 손실(

)은 수학식 5를 따를 수 있다.The time management model uses the first to fourth input features (IF1 to IF4) as input data and the remaining game length information as target data (

The output data (r) generated from the second time management neural network 530 through the first time management neural network 520 using the training data set of ) is the target data (

), the first time management neural network 520 and the second time management neural network 530 may be sufficiently learned. As an example, the time management model predicts the remaining match length loss (

) using remaining game length prediction loss (

) can be trained to be a minimum. For example, remaining match length prediction loss (

) may follow Equation 5.

(Equation 5)

는 집수의 변화량 손실이다. 남은 경기 길이 예측 손실(

)은 집수의 변화량 손실(

)을 이용한다. 남은 경기 길이 예측을 위하여 집수의 변화량을 이용하는 이유는 대국중 초반의 집수의 변화가 상대적으로 크고, 후반의 집수의 변화가 상대적으로 적을 가능성이 높으므로 남은 경기 길이 예측 판단의 요소로 사용할 수 있기 때문이다. 일 예로, 도 30를 참고하면, 도 30는 임의의 기보의 초반부의 바둑판 상태(S)에 대하여 형세 판단 모델 서버(400)를 이용하여 생성된 형세 판단 정보이다. 도 30(a)는 104수까지 둔 경우로 흑이 46집이고 백이 63.5집이 된다. 도 30(b)는 흑이 한수를 더 둔 105수까지 둔 경우로 흑이 46집이고 백이 59.5집으로 백의 경우 4집의 차이가 난다. 즉, 초반부의 바둑판 상태(S)는 한 수 둘때마다 집수의 변화량이 크다. 도 31를 참고하면, 도 31는 임의의 기보의 후반부의 바둑판 상태(S)에 대하여 형세 판단 모델 서버(400)를 이용하여 생성된 형세 판단 정보이다. 도 31(a)는 222수까지 둔 경우로 흑이 64집이고 백이 63.5집이 된다. 도 31(b)는 백이 한 수를 더 둔 223수까지 둔 경우로 흑이 63집이고 백이 64.5집으로 백의 경우 1집의 차이가 난다. 즉, 후반부의 바둑판 상태(S)는 한 수 둘때마다 집수의 변화량이 작다.in Equation 5

is the change loss of the catchment. Remaining match length prediction loss (

) is the change in catchment loss (

) is used. The reason for using the change in the catchment to predict the length of the remaining game is that the change in the catchment at the beginning of the game is relatively large and the change in the catchment in the second half is likely to be relatively small, so it can be used as a factor in determining the prediction of the remaining game length. am. As an example, referring to FIG. 30 , FIG. 30 is layout judgment information generated using the layout judgment model server 400 for the checkerboard state (S) at the beginning of any notation. In Fig. 30(a), in the case of up to 104 moves, black has 46 houses and white has 63.5 houses. 30(b) shows a case where black has 105 moves, one more, and black has 46 houses and white has 59.5 houses, so in the case of white, there is a difference of 4 houses. That is, in the checkerboard state (S) at the beginning of the game, the amount of change in the number of catches is large every two moves. Referring to FIG. 31 , FIG. 31 is position judgment information generated using the position determination model server 400 for the checkerboard state (S) of the second half of an arbitrary notation. In Figure 31(a), in the case of up to 222 moves, black is 64 houses and white is 63.5 houses. 31(b) shows the case where White adds one more move to 223 moves. Black has 63 houses and White has 64.5 houses, so in the case of White, there is a difference of one house. That is, in the checkerboard state (S) in the second half, the amount of change in the catchment is small for every two moves.

는 공배수 손실이다. 남은 경기 길이 예측 손실(

)은 공배수 손실(

)을 이용한다. 남은 경기 길이 예측을 위하여 공배수를 이용하는 이유는 대국 중에 후반으로 갈수록 공배수가 점점 줄어들기 때문에 이를 이용하여 남은 경기 길이 예측 판단의 요소로 사용할 수 있기 때문이다. 일 예로, 도 32을 참고하면, 도 32은 임의의 기보의 초반부와 후반부의의 바둑판 상태(S)에 대하여 형세 판단 모델 서버(400)를 이용하여 생성된 형세 판단 정보이다. 공배는 도 32의 형세 판단 결과에서 붉은 선으로 X표시가 된 곳이다. 도 32(a)는 대국의 초반부이기 때문에 공배가 많다. 도 32(b)는 대국의 후반부이기 때문에 공배가 적다.in Equation 5

is the common multiple loss. Remaining match length prediction loss (

) is the common multiple loss (

) is used. The reason why the common multiple is used to predict the remaining game length is that it can be used as a factor in determining the remaining game length prediction as the common multiple gradually decreases toward the second half of the game. As an example, referring to FIG. 32 , FIG. 32 is the layout judgment information generated using the layout judgment model server 400 for the checkerboard state (S) of the first and second half of any notation. Gongbae is a place marked with a red line X in the situation judgment result of FIG. 32 . Figure 32 (a) is the first half of the game, so there are many common games. Figure 32 (b) is the second half of the game, so there are few public games.

는 가치값 손실이다. 남은 경기 길이 예측 손실(

)은 가치값 손실(

)을 이용한다. 남은 경기 길이 예측을 위하여 가치값 이용하는 이유는 대국 중에 후반으로 갈수록 어느 한쪽의 가치값이 높아지기 때문에 이를 이용하여 남은 경기 길이 예측 판단의 요소로 사용할 수 잇기 때문이다. 또한, 가치값 손실(

)은 초반에는 큰 변화가 없기 때문에 경기 후반부에 이용될 수 있다. 예를 들어, 경기 후반부는 임의의 기보에서 250수이상의 착수가 된 바둑판 상태일 수 있고, 이에 제한되는 것은 아니다.in Equation 5

is the value loss. Remaining match length prediction loss (

) is the value loss (

) is used. The reason why the value value is used to predict the length of the remaining game is that it can be used as an element of predicting the length of the remaining game by using this value because the value of one side increases toward the second half of the game. In addition, value loss (

) can be used later in the game because there is no significant change in the early stages. For example, the second half of the game may be a checkerboard state in which more than 250 numbers have been set out in any notation, but is not limited thereto.

는 하이퍼 파라미터들이다. 사용자는 하이퍼 파라미터는 조절하여 각 손실의 상대적인 중요도를 조절할 수 있다. 예를 들어,

는 경기 후반부로 갈수록 중요도가 높아지므로 수치가 높아질 수 있고, 경기 초반부에는 중요도가 낮으므로 수치가 낮을 수 있다.In Equation 5,

are hyperparameters. The user can adjust the relative importance of each loss by adjusting the hyperparameters. for example,

The value may increase as the importance increases towards the later part of the game, and the value may decrease due to its low importance in the early part of the game.

The learned time management model may generate game time information about the predicted remaining game length when a current checkerboard state is input during a game. More specifically, the learned time management model may extract a first input feature by the first input feature extractor 540 when a current checkerboard state is input during a game. The time management model may use change amount information of collections provided by the situation judgment model server as a situation judgment for the current checkerboard state as a second input feature, and common multiple information as a third input feature. The time management model may provide an output value generated by the first time management neural network 520 to the second time management neural network 530 by using the first to third input characteristics as input data. The time management model may use, as a fourth input characteristic, a value for a starting candidate point in the current checkerboard state provided by the starting model server. The time management model may generate game time information about the remaining game length predicted by the second time management neural network 530 by using the output value of the first time management neural network 520 and the fourth input feature as input data.

In addition, the time management model server 500 may adjust the starting preparation time according to the predicted remaining game length of the game time information. The time management model server 500 may provide the adjusted start preparation time to the start model server 300 .

Therefore, the deep learning-based Go game service device according to another embodiment may predict the remaining game length. In addition, the deep learning-based Go game service apparatus according to another embodiment can effectively divide the start preparation time by using the predicted remaining game length.

33 is an exemplary diagram of a signal flow in a Go game service system of a time management model server according to another embodiment of the present invention.

Referring to FIG. 33, the Monarch server 200 may transmit a plurality of notations to the starting model server 300, the situation judgment model server 400, and the time management model server 500 (S2701). The time management model server 500 may extract the first input feature of the checkerboard state of the plurality of notations received (S2702). The situation judgment model server 400 may generate situation judgment information of a plurality of notations received and transmit the situation judgment information to the time management model server 500 (S2703 and S2704). The situation determination information may be change amount information and common multiple information of collections. The time management model server 500 may input the variation information of collections as the second input feature and the common multiple information as the third input feature to the time management first neural network of the time management model (S2705). Embarkation model server 300 may generate a value value according to the checkerboard state of a plurality of notation, and transmit the generated value value to the time management model server 500 (S2706, S2707). The time management model server 500 may input the received value as a fourth input feature to the second neural network for time management of the time management model (S2708). The time management model server 500 may train a time management model using the input data of the first to fourth input characteristics and remaining game length information according to the checkerboard state of a plurality of notations as target data (S2709). The Go server 200 proceeds with the Go game, and the terminal 100 and the start model server 300 may perform a start on their turn (S2710 to S2712). The layout judgment model server 400 extracts the input features of the current checkerboard state, the layout judgment model, which is a deep learning model, generates a layout value using the input features, and performs the layout judgment using the checkerboard status and the layout values. It can (S2713). The time management model server 500 extracts the input feature of the current checkerboard state as the first input feature, uses the situation determination information as the second and third input features, and sets the value provided by the start model server 300 to the first input feature. As a fourth input feature, a time management model, which is a deep learning model, may generate game time information (S2714).

34 is a method for generating game time information among deep learning-based Go game service methods according to another embodiment of the present invention.

Referring to FIG. 34 , the deep learning-based Go game service method according to another embodiment of the present invention may include a step (S2801) of receiving a plurality of notations by the time management model server 500. A description of a plurality of notations follows the description of FIG. 29 .

The deep learning-based Go game service method according to another embodiment of the present invention may include extracting a first input feature from the checkerboard state of a plurality of notations received by the time management model server 500 (s2802). there is. A method of extracting the first input feature follows the description of FIG. 29 . The time management model server 500 may provide the extracted first input feature as input data of the first time management neural network.

The deep learning-based Go game service method according to another embodiment of the present invention may include a step (S2803) of receiving, by the time management model server 500, position determination information according to a checkerboard state of a plurality of notations. A method of receiving situation determination information follows the description of FIG. 29 . The situation determination information may be information on the amount of change in the number of catchments and information on common multiples. The time management model server 500 may use change amount information of collections as a second input feature and common multiple information as a third input feature.

A deep learning-based Go game service method according to another embodiment of the present invention includes inputting, by the time management model server 500, a second input feature and a third input feature to a first time management neural network (S2804). can do.

The deep learning-based Go game service method according to another embodiment of the present invention may include a step of receiving, by the time management model server 500, a value from the start model server (S2805). The time management model server 500 may use a value as a fourth input feature.

The deep learning-based Go game service method according to another embodiment of the present invention may include inputting, by the time management model server 500, a fourth input characteristic to a second time management neural network (S2806).

In the deep learning-based Go game service method according to another embodiment of the present invention, the time management model server 500 receives the input data of the first to fourth input characteristics and the remaining game length information according to the checkerboard state of a plurality of notations. A step of training a time management model using target data (S2807) may be included. The training method of the time management model follows the description of FIGS. 29 to 32 .

In the deep learning-based Go game service method according to another embodiment of the present invention, the time management model server 500 may include a step (S2809) of receiving a checkerboard state, situation judgment information, and a value value during a game. .

In the deep learning-based Go game service method according to another embodiment of the present invention, the time management model server 500 generates game time information using the received checkerboard state, situation judgment information, and value (S2810) can include A method of generating game time information follows the description of FIG. 29 .

Therefore, the deep learning-based Go game service method according to another embodiment can predict the remaining game time. In addition, the deep learning-based Go game service method according to another embodiment can effectively divide the start preparation time using the predicted remaining game time.

Embodiments according to the present invention described above may be implemented in the form of program instructions that can be executed through various computer components and recorded on a computer-readable recording medium. The computer readable recording medium may include program instructions, data files, data structures, etc. alone or in combination. Program instructions recorded on the computer-readable recording medium may be specially designed and configured for the present invention, or may be known and usable to those skilled in the art of computer software. Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks and magnetic tapes, optical recording media such as CD-ROMs and DVDs, and magneto-optical media such as floptical disks. medium), and hardware devices specially configured to store and execute program instructions, such as ROM, RAM, flash memory, and the like. Examples of program instructions include high-level language codes that can be executed by a computer using an interpreter or the like as well as machine language codes generated by a compiler. A hardware device may be modified with one or more software modules to perform processing according to the present invention and vice versa.

Specific implementations described in the present invention are examples and do not limit the scope of the present invention in any way. For brevity of the specification, description of conventional electronic components, control systems, software, and other functional aspects of the systems may be omitted. In addition, the connection of lines or connecting members between the components shown in the drawings are examples of functional connections and / or physical or circuit connections, which can be replaced in actual devices or additional various functional connections, physical connection, or circuit connections. In addition, if there is no specific reference such as “essential” or “important”, it may not be a component necessarily required for the application of the present invention.

In addition, the detailed description of the present invention described has been described with reference to preferred embodiments of the present invention, but those skilled in the art or those having ordinary knowledge in the art will find the spirit of the present invention described in the claims to be described later. And it will be understood that the present invention can be variously modified and changed without departing from the technical scope. Therefore, the technical scope of the present invention is not limited to the contents described in the detailed description of the specification, but should be defined by the claims.

100 terminals
200 Go server
300 launch model server
310 search unit
320 Self Play Department
330 Initiate Neural Networks
400 Layout Model Server
410 Neural Networks
420 input feature extraction unit
430 Correct Answer Label Generation Unit
500 hour management model server
510 Time Management Division
520 Time Management First Neural Network
530 Time Management Secondary Neural Network
540 first input feature extraction unit
550 time adjustment unit

Claims (12)

a communication unit for receiving at least one of a value value and a relative start time according to a checkerboard state;
a memory for storing the time adjustment unit; and
And a processor for reading the time adjustment unit and controlling the time adjustment unit to determine a start preparation time using at least one of the value value and the relative start time.
The time adjustment unit,
Determining at least one of the change trend and the change range of the value value, and determining the start preparation time according to the determined change trend and change range
Computation time management device. delete ◈Claim 3 was abandoned when the registration fee was paid.◈ According to claim 1,
The time adjustment unit,
If the change rate of the value value, which is the trend of change of the value value, is less than a predetermined value, increasing the start preparation time
Computation time management device. ◈Claim 4 was abandoned when the registration fee was paid.◈ According to claim 1,
The time adjustment unit,
If the range of change between the value value in the previous start step and the current value value decreases by more than a predetermined value, increasing the start preparation time
Computation time management device. a communication unit for receiving at least one of a value value and a relative start time according to a checkerboard state;
a memory for storing the time adjustment unit; and
And a processor for reading the time adjustment unit and controlling the time adjustment unit to determine a start preparation time using at least one of the value value and the relative start time.
The time adjustment unit,
Based on the relative start time, the average relative start time, which is the average of the previous relative start time, which is the relative start time required for the other party's previous start, and the relative start time required for a plurality of starts performed by the other party during a predetermined point in time yielding
Computation time management device. According to claim 5,
The time adjustment unit,
If the previous relative start time is greater than the average relative start time, increasing the start preparation time
Computation time management device. ◈Claim 7 was abandoned when the registration fee was paid.◈ The method of any one of claims 3, 4 and 6,
The time adjustment unit,
When the condition for increasing the start preparation time is satisfied, calculating the start time increase amount which is a standard for increasing the start preparation time
Computation time management device. ◈Claim 8 was abandoned when the registration fee was paid.◈ According to claim 7,
The memory further includes a time management unit providing a basic start time that is a preset start preparation time,
The time adjustment unit,
Determining the start time increment based on the basic start time provided by the time management unit
Computation time management device. ◈Claim 9 was abandoned when the registration fee was paid.◈ According to claim 8,
The time adjustment unit,
Determining the start time increment based on the preset countdown time and the basic start time
Computation time management device. ◈Claim 10 was abandoned when the registration fee was paid.◈ According to claim 6,
The memory further includes a time management unit providing a basic start time that is a preset start preparation time,
The time adjustment unit,
Determining the larger start time of the previous relative start time and the basic start time provided by the time management unit as the start preparation time
Computation time management device. According to claim 6,
The time adjustment unit,
Calculate the relative start time ratio, which is the ratio of the previous relative start time to the average relative start time,
The memory further includes a time management unit providing a basic start time that is a preset start preparation time,
The time adjustment unit,
When the relative start time ratio meets a predetermined criterion, determining the start preparation time by performing a predetermined operation based on the relative start time ratio and the basic start time provided by the time management unit
Computation time management device. As a method for managing the computation time of a deep learning-based Go game service in a time management model server,
Receiving at least one of a value value and a relative starting time according to a checkerboard state; And determining a start preparation time using at least one of the received value value and the relative start time;
The step of determining the start preparation time,
Determining at least one of a change trend and a change range of the value, and determining the start preparation time according to the determined change trend and change range;
Based on the relative start time, the average relative start time, which is the average of the relative start time immediately before the opponent's previous start and the relative start time required for the plurality of starts performed by the other party during a predetermined point in time, is calculated. And determining the start preparation time based on the calculated relative start time immediately before and the average relative start time
Operation time management method.

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