JP2011248549A - Interactive device, interactive method, and program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide an interactive device capable of assisting user's decision making.SOLUTION: The interactive device capable of assisting user's decision making comprises a knowledge database in which two or more pieces of spot information having a spot, one or more determinants, and an evaluation value of each determinant are stored; an information recommendation method storage unit for storing two or more information recommendation methods including sentence pattern information and evaluation information of a sentence output; a user state information storage unit for storing user state information including a user preference vector for the determinant and a user knowledge vector for the determinant; a reception unit for receiving a sentence from a user; a score calculation unit for calculating two or more scores by applying the user state information to the evaluation information of the two or more respective information recommendation methods; a sentence configuration unit for configuring a sentence from the sentence pattern information which is included in the information recommendation method having the maximum score of 1; a sentence output unit that outputs the sentence; and a user state information update unit for updating the user state information by acquiring one or more determinant from the received sentence or the output sentence.

Description

  The present invention relates to an interactive device or the like that interacts with a user, and relates to an interactive device that supports a user's decision making.

  A consultation-type spoken dialogue system is considered to be a kind of decision support system. There are many cases of decision support in the field of operations research, and a representative method is the hierarchical analysis method (AHP method) (Non-patent Document 1). In the AHP method, the problem elements are divided into three layers, “Final Goal”, “Evaluation Criteria”, and “Alternative”, and the optimal weight is determined by estimating the local weight (importance) of each user's evaluation criteria. Do.

  It is conceivable to apply the AHP method as it is to a dialogue apparatus.

  Conventionally, there has been an interactive device that retrieves and presents information corresponding to a key input by a user.

Saaty, T., The Analytic Hierarchy Process: Planning, Priority Setting, Resource Allocation, Mcgraw-Hill (1980)

However, when this AHP method is applied as it is to a dialog device, a dialog device that can achieve the purpose of supporting decision making by the user cannot be constructed. That is, in order to make an optimal decision for the user, first, the weight P _user = (p ₁ , p ₂ ,..., P _M ) for the evaluation criterion, and the local from the viewpoint of each evaluation criterion for each alternative The weights V _user = (v ₁₁ , v ₁₂ ,..., V _1M ,..., V _nm ) are determined. The determination of the optimal candidate is realized by selecting an alternative k that maximizes the priority “Σ _{m = 1} ^M p _m v _km ”. In the general AHP method, the above weights are determined by paired comparisons with evaluation criteria and alternatives.

  However, candidates and domain knowledge that can be presented by the device to the user are often information that can be known for the first time through the dialogue, and are rarely known at the beginning of the dialogue. In many cases, the dialogue apparatus handles a large number of candidates (alternatives) and evaluation criteria. Under such circumstances, it is not realistic to perform a paired comparison because a large number of exchanges are required.

  In addition, an interactive device that only presents information corresponding to a key input by the user cannot achieve the purpose of supporting the user's decision making.

  The interactive apparatus according to the first aspect of the present invention is an interactive apparatus that interacts with a user regarding a spot, and includes a spot, one or more determinants for determining a spot, and one or more determinants of a spot. A knowledge base that stores two or more spot information having an evaluation value indicating evaluation, sentence pattern information that is information indicating a sentence output by the interactive device or a sentence pattern output by the interactive device, and sentence pattern information An information recommendation method storage unit that stores two or more information recommendation methods having evaluation information of sentence pattern information used when selecting an item, information indicating a user's state, and one or more determinants A user status storing user state information having a preference vector which is information indicating a user's preference with respect to a user, and a knowledge vector indicating a user's knowledge with respect to one or more determinants An information storage unit, a reception unit that accepts a sentence input by a user, and user status information are applied to evaluation information included in each of two or more information recommendation methods stored in the information recommendation method storage unit. Using the score calculation unit that calculates two or more scores for the information recommendation method and the two or more scores calculated by the score calculation unit, the sentence pattern information possessed by one information recommendation method is acquired, and a sentence is obtained from the sentence pattern information. At least one sentence out of one or more sentences out of a sentence composition part, a sentence output part that outputs a sentence composed by the sentence composition part, a sentence accepted by the reception part, or a sentence output by the sentence output part A user status information update unit that acquires a spot or one or more determinants and updates the user status information in the user status information storage unit using one or more spots or one or more determinants, and scores The output unit applies the user status information updated by the user status information update unit to evaluation information of each of the two or more information recommendation methods stored in the information recommendation method storage unit, and applies to the two or more information recommendation methods It is an interactive device that calculates a score of 2 or more.

  With this configuration, it is possible to appropriately support the user's decision making by performing the dialogue while dynamically changing the information about the user's knowledge and preferences according to the progress of the dialogue with the user.

  Further, the dialog device according to the second aspect of the present invention is the dialog device having the dialogue amount information, which is information indicating the amount of dialogue between the dialogue device and the user up to the present time, with respect to the first invention. is there.

  With this configuration, it is possible to appropriately support the user's decision making by performing the dialogue while dynamically changing the information about the user's knowledge and preferences according to the progress of the dialogue with the user.

  Further, in the dialog device according to the third aspect of the invention, in contrast to the first or second invention, the user status information is received from the user immediately before or after the information related to the determinant corresponding to the sentence output by the dialog device. This is a dialogue apparatus including one or more pieces of information related to the determinant corresponding to the sentence.

  With this configuration, it is possible to appropriately support the user's decision making by performing the dialogue while dynamically changing the information about the user's knowledge and preferences according to the progress of the dialogue with the user.

  Further, in the interactive apparatus according to the fourth aspect of the present invention, with respect to any one of the first to third aspects, the user status information relates to one or more local weights indicating the importance of the spot from the viewpoint of the user's determinant. It is an interactive device including local weight information which is information.

  With this configuration, it is possible to appropriately support the user's decision making by performing the dialogue while dynamically changing the information about the user's knowledge and preferences according to the progress of the dialogue with the user.

  In the dialogue apparatus according to the fifth aspect of the present invention, the information recommendation method storage unit recommends detailed description of the sentence pattern information and the sentence for the spot described immediately before the first to fourth aspects of the invention. Current topic spot information recommendation method having pattern information evaluation information, current topic determinant information recommendation having sentence pattern information recommending another tourist spot related to the determinant just described and sentence pattern information evaluation information The dialogue apparatus stores at least a technique and an open prompt information recommendation technique having sentence pattern information indicating an open prompt and evaluation information of the sentence pattern information without particularly recommending information.

  With this configuration, it is possible to appropriately support the user's decision making by performing the dialogue while dynamically changing the information about the user's knowledge and preferences according to the progress of the dialogue with the user.

  Further, in the dialog device according to the sixth invention, the sentence composing unit corresponds to the largest score among the two or more scores calculated by the score calculating unit with respect to any one of the first to fourth inventions. A sentence pattern information acquisition unit that acquires sentence pattern information included in the information recommendation method, one or more variables included in the sentence pattern information acquired by the sentence pattern information acquisition unit, and a spot or a determination factor corresponding to the variable, Variable value acquisition means for acquiring from one or more of the sentences output immediately before by the sentence output section or the sentence received by the reception section, and the location of the variable in the sentence pattern information acquired by the sentence pattern information acquisition means And a sentence constructing means for constructing a sentence by inserting the term obtained by the variable value obtaining means.

  With this configuration, it is possible to appropriately support the user's decision making by performing the dialogue while dynamically changing the information about the user's knowledge and preferences according to the progress of the dialogue with the user.

  In the dialog device according to the seventh aspect of the invention, in contrast to the sixth aspect, the variable value acquisition means acquires one or more variables included in the sentence pattern information acquired by the sentence pattern information acquisition means, One or more spots or one or more determinants to be the corresponding candidates are acquired from one or more sentences of the sentence output immediately before by the sentence output unit or the sentence received immediately before by the reception unit and become candidates. An interactive device that selects a spot or determinant corresponding to a variable from one or more spots or one or more determinants using a candidate evaluation value of one or more spots or one or more determinants. It is.

  With this configuration, it is possible to appropriately support the user's decision making by performing the dialogue while dynamically changing the information about the user's knowledge and preferences according to the progress of the dialogue with the user.

  Further, in the dialog device according to the eighth aspect of the present invention, in response to any one of the first to seventh aspects, the user state information update unit obtains at least one or more determinants from the sentence received by the reception unit. A term acquisition unit, a device presentation term acquisition unit that acquires at least one or more determinants from one or more of the sentences output by the sentence output unit, and one or more determinants acquired by the user presentation term acquisition unit Preference vector update means for updating so as to increase the value of the element of the preference vector with respect to, and knowledge vector update for updating so as to increase the value of the element of the knowledge vector for one or more determinants acquired by the device presentation term acquisition means An interactive device.

  With this configuration, it is possible to appropriately support the user's decision making by performing the dialogue while dynamically changing the information about the user's knowledge and preferences according to the progress of the dialogue with the user.

Further, according to the ninth aspect of the present invention, in any one of the first to eighth aspects, the accepting unit recognizes the speech inputted by the user and recognizes the speech and converts it into a character string. And a sentence output unit is an interactive device that outputs a sentence constituted by the sentence constructing unit.
With this configuration, it is possible to appropriately support the user's decision making by performing the voice conversation while dynamically changing the information related to the user's knowledge and preferences according to the progress of the voice conversation with the user.

  According to the dialogue apparatus according to the present invention, it is possible to appropriately support the user's decision making by performing the dialogue while dynamically changing the information related to the user's knowledge and preferences according to the progress of the dialogue with the user.

Block diagram of interactive apparatus according to Embodiment 1 A flowchart for explaining the operation of the dialogue apparatus A flowchart for explaining the operation of the score calculation process Flowchart explaining operation of same sentence composition processing A flowchart for explaining the operation of the recommended sentence acquisition process A flowchart for explaining the operation of the user status information update process The figure which shows the conceptual diagram of the dialogue apparatus 1 Figure showing the spot information management table Figure showing the information recommendation method management table Diagram explaining the flow of the dialogue Overview of the computer system Block diagram of the computer system

  Hereinafter, embodiments of an interactive device and the like will be described with reference to the drawings. In addition, since the component which attached | subjected the same code | symbol in embodiment performs the same operation | movement, description may be abbreviate | omitted again.

  (Embodiment 1)

  In the present embodiment, a consultation-type dialogue apparatus will be described in which a user selects a candidate while receiving information presentation or information recommendation from the apparatus. In the dialogue apparatus according to the present embodiment, interaction with the user is performed by voice, but voice input / output is not essential.

  When selecting a candidate that suits the taste, it is necessary to consider many factors (same meaning as a determinant described later). Since the user who uses the interactive device does not necessarily grasp all such factors, the interactive device recommends information to the user, and the gap between the knowledge held by the interactive device and the user's knowledge is obtained. Need to fill.

  In this embodiment, an interactive apparatus that implements a consultation-type conversation model for selecting a candidate suitable for the user from among a plurality of candidates (here, mainly referred to as spots) will be described.

  FIG. 1 is a block diagram of an interactive apparatus 1 in the present embodiment. The dialogue apparatus 1 includes a knowledge base 11, an information recommendation method storage unit 12, a user state information storage unit 13, a reception unit 14, a score calculation unit 15, a sentence composition unit 16, a sentence output unit 17, and a user state information update unit 18. .

  The reception unit 14 includes a voice reception unit 141 and a voice recognition unit 142.

  The sentence composition unit 16 includes a sentence pattern information acquisition unit 161, a variable value acquisition unit 162, and a sentence configuration unit 163.

  The user status information update unit 18 includes a user presentation term acquisition unit 181, a device presentation term acquisition unit 182, a preference vector update unit 183, and a knowledge vector update unit 184.

  The knowledge base 11 stores two or more pieces of spot information. The spot information is information having a spot, one or more determinants, and an evaluation value. A spot has the same meaning as information for identifying a spot, and is, for example, a spot name. A spot is a place where a user usually visits, such as a sightseeing spot, a restaurant, or a store. However, anything can be used as long as it is a subject of dialogue. For example, a spot may be a company, a group of people, a concept, etc., and is widely understood. The determinant is a factor for determining a spot. The determinant can also be said to be an attribute of a spot. The determinant can be said to be the viewpoint of spot evaluation. When the spot is a sightseeing spot, the determining factors are, for example, “famous for gardens”, “not crowded”, “world heritage”, “scenic scenery”, and the like. The evaluation value is information indicating evaluation of one or more determinants of the spot. The evaluation value may be binary (two stages) such as “1 (◯), 0 (×)”, or may be multistage such as an integer of 1 to 5. The evaluation value is an evaluation value indicating whether or not a spot is a determining factor. The spot information may have a description corresponding to the spot and the determining factor. When spot information has an explanatory note, it usually has an explanatory note for each spot and one determining factor.

  The knowledge base 11 is preferably a non-volatile recording medium, but can also be realized by a volatile recording medium. The process of storing spot information in the knowledge base 11 does not matter. For example, spot information may be stored in the knowledge base 11 via a recording medium, or spot information transmitted via a communication line or the like may be stored in the knowledge base 11, Alternatively, spot information input via an input device may be stored in the knowledge base 11.

  The information recommendation method storage unit 12 stores two or more information recommendation methods. The information recommendation method is information having sentence pattern information and sentence pattern information evaluation information. The sentence pattern information is information indicating a sentence output from the interactive device 1 or a sentence pattern output from the interactive device 1. Sentence pattern information is a term included in a sentence received from a user, a spot of interest, a term included in a sentence output by the system (dialogue device 1), and one or more information of one or more determinants as variables. Information. The evaluation information of sentence pattern information is information used when selecting sentence pattern information. The evaluation information is information that is a source of calculating a score indicating the degree to which each information recommendation method should be selected for the latest user state information, and is information associated with the sentence pattern information. The evaluation information is, for example, a vector that is multiplied with the user state information.

  In addition, the information recommendation method includes the following six methods, for example. (1) Information recommendation regarding current topic spot (method 1), (2) Information recommendation regarding current topic determinants (method 2), (3) Open prompt (method 3), (4) Presentation of determinants 1 ( Method 4), (5) Presentation of decision factor 2 (Method 5), (6) Spot recommendation (Method 6) that the user is estimated to be interested in. Method 1 is a current topic spot information recommendation method having sentence pattern information for recommending detailed explanation and evaluation information of sentence pattern information for the spot explained immediately before. Method 2 is a current topic determinant information recommendation method having sentence pattern information for recommending another tourist spot related to the determinant described immediately before and evaluation information of the sentence pattern information. Method 3 is an open prompt information recommendation method that does not recommend information in particular and includes sentence pattern information indicating an open prompt and evaluation information of the sentence pattern information. Method 4 is a method for presenting determinants that can be explained by the dialogue apparatus 1. When the method 4 is selected, the user selects from the determinants having a low knowledge of the user estimated by the dialogue apparatus 1. Method 5 is a method for presenting determinants that can be explained by the dialogue apparatus 1, as in Method 4. When the method 5 is selected, the user selects from the determination factors having high knowledge of the user estimated by the dialogue apparatus 1. Method 6 is a method of selecting a spot k that the user is most interested in based on the user's interest estimated by the interactive apparatus 1 and presenting the spot k to the user.

  The information recommendation method storage unit 12 is preferably a non-volatile recording medium, but can also be realized by a volatile recording medium.

  The process in which the information recommendation method is stored in the information recommendation method storage unit 12 does not matter. For example, the information recommendation method may be stored in the information recommendation method storage unit 12 via a recording medium, and the information recommendation method transmitted via a communication line or the like is stored in the information recommendation method storage unit 12. Alternatively, the information recommendation method input via the input device may be stored in the information recommendation method storage unit 12.

  The user state information storage unit 13 is information indicating a user's state, a preference vector that is information indicating a user's preference with respect to one or more determinants, and a knowledge vector indicating user's knowledge with respect to one or more determinants The user status information including

It is preferable that the user state information includes local weight information that is information on one or more local weights indicating the importance of the spot from the viewpoint of the user's determinant. The local weight v _nm for the spot n from the viewpoint of the user's determinant m is, for example, when the interactive apparatus 1 informs the user of the spot evaluation using the information recommendation method 1, the information recommendation method 2, or the information recommendation method 6. “1” shall be taken. This is based on the assumption that the user determines only from the information presented from the interactive device 1. Further, the user status information in the user status information storage unit 13 is dynamically changed as the dialogue progresses.

  The user status information storage unit 13 is preferably a non-volatile recording medium, but can also be realized by a volatile recording medium.

  The process in which the user status information is stored in the user status information storage unit 13 does not matter. For example, user state information may be stored in the user state information storage unit 13 via a recording medium, and user state information transmitted via a communication line or the like is stored in the user state information storage unit 13. Alternatively, the user status information input via the input device may be stored in the user status information storage unit 13.

  The reception unit 14 receives a sentence input by the user. Here, the reception is usually reception of voice. However, reception means reception of information input from an input device such as a keyboard, mouse, touch panel, reception of information transmitted via a wired or wireless communication line, a recording medium such as an optical disk, a magnetic disk, or a semiconductor memory This is a concept including reception of information read out from. In other words, the input includes voice input, character string input, and the like, and any input means may be used.

  The sentence input means may be anything such as a microphone, keyboard, mouse or menu screen. The receiving unit 14 can be realized by a device driver for input means such as a microphone and a keyboard, control software for a menu screen, and the like.

  The voice receiving unit 141 receives voice input by the user from a microphone.

  The voice recognition unit 142 recognizes the voice received by the voice reception unit 141 and converts it into a character string. The voice recognition method in the voice recognition unit 142 is not limited. Since the voice recognition unit 142 is a known technique, a detailed description thereof is omitted. The voice recognition unit 142 can be usually realized by an MPU, a memory, or the like. The processing procedure of the voice recognition unit 142 is usually realized by software, and the software is recorded in a recording medium such as a ROM. However, it may be realized by hardware (dedicated circuit).

  The score calculation unit 15 applies the user state information stored in the user state information storage unit 13 to evaluation information included in each of the two or more information recommendation methods stored in the information recommendation method storage unit 12, and each of the two or more information recommendations A score of 2 or more for the method is calculated. The score calculation unit 15 applies the user state information updated by the user state information update unit 18 to evaluation information included in each of the two or more information recommendation methods stored in the information recommendation method storage unit 12. A score of 2 or more for each information recommendation method is calculated.

  The score calculation unit 15 normally calculates a score before the sentence output unit 17 outputs a sentence (not necessarily immediately before). Note that it is preferable that the score calculation unit 15 calculates a score every time the reception unit 14 receives a sentence. Here, the application is, for example, calculating a score by an arithmetic expression “score = f (user state information, evaluation information)”. For example, f is “score = user state information × evaluation information”. That is, the score calculation unit 15 uses the evaluation information managed in association with the sentence pattern information and the user state information that dynamically changes in order to determine the sentence pattern information of the sentence to be output next by the dialogue apparatus 1. Are used to calculate a score for each information recommendation method.

  The score calculation unit 15 can usually be realized by an MPU, a memory, or the like. The processing procedure of the score calculation unit 15 is usually realized by software, and the software is recorded in a recording medium such as a ROM. However, it may be realized by hardware (dedicated circuit).

  The sentence composing unit 16 acquires sentence pattern information included in one information recommendation method using two or more scores calculated by the score calculating unit 15, and composes a sentence from the sentence pattern information. Normally, the sentence composing unit 16 acquires sentence pattern information included in the information recommendation method having the maximum score calculated by the score calculating unit 15 and composes a sentence from the sentence pattern information. Here, when the sentence pattern information is a sentence, the structure of the sentence is NOP (no processing is performed). In addition, if the sentence pattern information is pattern information of a sentence including variables, the sentence structure is determined from the immediately preceding output sentence, the immediately preceding accepted sentence, the determinant of interest, the spot of interest, etc. This is a process of acquiring the value of a variable and substituting the acquired value of the variable into sentence pattern information. Note that the determinant of interest is the determinant from which one or more spots are output. The spot of interest is a spot from which one or more determinants are output. In addition, the sentence structure part 16 also performs the process which acquires an attention spot and an attention determination factor from the user's input sentence immediately before and / or the output sentence of the dialog apparatus 1 immediately before. Details of such processing will be described later.

  The sentence constructing unit 16 can be usually realized by an MPU, a memory or the like. The processing procedure of the sentence composing unit 16 is usually realized by software, and the software is recorded in a recording medium such as a ROM. However, it may be realized by hardware (dedicated circuit).

  The sentence pattern information acquisition unit 161 acquires, from the information recommendation method storage unit 12, sentence pattern information included in one information recommendation method corresponding to the largest score among the two or more scores calculated by the score calculation unit 15.

  The variable value acquisition unit 162 acquires one or more variables included in the sentence pattern information acquired by the sentence pattern information acquisition unit 161, and the sentence output unit 17 outputs the spot or determination factor corresponding to the variable immediately before. Or from one or more of the sentences received immediately before by the receiving unit 14. Further, the variable value acquisition unit 162 acquires one or more variables included in the sentence pattern information acquired by the sentence pattern information acquisition unit 161, and sets one or more spots or one or more determinants as candidates corresponding to the variables. From one or more of the sentences output immediately before by the sentence output unit 17 or the sentence received immediately before by the reception unit 14, and from one or more spots or one or more determinants as candidates, Using the evaluation values of the knowledge base 11 corresponding to one or more spots or one or more determinants, a spot or determinant corresponding to the variable is selected. The variable value acquisition unit 162 may acquire a spot or a determinant corresponding to the variable from a target spot that is a spot that is currently focused or a focus determination factor that is a decisive factor that is currently focused.

  The sentence composition unit 163 composes a sentence by inserting the term (usually a spot or a determinant) acquired by the variable value acquisition unit 162 into the variable part of the sentence pattern information acquired by the sentence pattern information acquisition unit 161. The sentence composing unit 163 may perform a transformation process so that the output sentence becomes a natural and fluent sentence. Since this processing is a known technique, detailed description thereof is omitted.

  The sentence output unit 17 outputs the sentence constructed by the sentence constructing unit 16. Here, the output is usually an audio output. That is, it is preferable that the sentence output unit 17 outputs the sentence formed by the sentence structure unit 16 by voice. However, output refers to display on a display, projection using a projector, printing on a printer, transmission to an external device (such as an audio output device or display device), storage in a recording medium, other processing devices, etc. This is a concept that includes delivery of processing results to other programs.

  The sentence output unit 17 may or may not include an output device such as a display or a speaker. The sentence output unit 17 may be implemented by output device driver software, or output device driver software and an output device.

  The user status information update unit 18 acquires at least one spot or one or more determinants from one or more sentences out of the sentences received by the reception unit 14 or the sentences output by the sentence output unit 17. The user status information in the user status information storage unit 13 is updated using one or more spots or one or more determining factors. The user state information update unit 18 performs morphological analysis on one or more sentences of the sentence received by the reception part 14 or the sentence output by the sentence output part 17 and stores a specific part of speech (noun, adjective, adjective verb, etc.). The term may be acquired, the knowledge base 11 may be searched using the term as a key, and the term stored in the knowledge base 11 may be acquired as a spot or a determinant. The user status information update unit 18 acquires a kanji string from one or more sentences out of the sentences received by the receiving unit 14 or the sentences output by the sentence output unit 17, and uses the kanji string as a key as a knowledge base. 11 may be retrieved, and a Chinese character string stored in the knowledge base 11 may be acquired as a spot or a determining factor. Then, the user state information update unit 18 usually updates the user state information so that the values of elements (elements such as a preference vector and a knowledge vector) constituting the user state information regarding the acquired spot or determination factor are increased. . Moreover, the user status information update unit 18 is normally updated every time the receiving unit 14 receives a sentence. However, the user state information update unit 18 may update the sentence output unit 17 each time a sentence is output.

  The user status information update unit 18 can be usually realized by an MPU, a memory, or the like. The processing procedure of the user status information update unit 18 is usually realized by software, and the software is recorded on a recording medium such as a ROM. However, it may be realized by hardware (dedicated circuit).

  The user presented term acquisition means 181 acquires at least one or more determinants from the sentence received by the receiving unit 14. The user-presented term acquisition unit 181 may acquire only a positive determinant, or may detect affirmation / negative and acquire a determinant for each category (positive / negative). For example, the user-presented term acquisition unit 181 acquires an independent word from a sentence received by the receiving unit 14, and acquires the independent word as a determining factor when the independent word is a determining factor of the knowledge base 11. Further, the user presented term acquisition unit 181 may acquire a focus determination factor.

  The device presentation term acquisition unit 182 acquires at least one or more determination factors from one or more sentences out of the sentences output by the sentence output unit 17. For example, the device presentation term acquisition unit 182 acquires an independent word from a sentence output by the sentence output unit 17 and acquires the independent word as a determining factor when the independent word is a determining factor of the knowledge base 11. Note that the sentence output by the sentence output unit 17 has the same meaning as the sentence configured by the sentence configuration unit 16. Further, the device presentation term acquisition unit 182 may acquire only a positive determination factor, or may detect affirmation / negative and acquire a determination factor for each category (positive / negative). In addition, the device presentation term acquisition unit 182 may acquire a focus determination factor.

  The preference vector update unit 183 updates the user state information so as to increase the value of the preference vector element for one or more determination factors acquired by the user presented term acquisition unit 181. Also, the preference vector update unit 183 is a value of an element of the preference vector for one or more determinants that cannot be acquired by the user-presented term acquisition unit 181 among one or more determinants acquired by the device presentation term acquisition unit 182. The user status information is updated so as to lower the value. This is to lower the value of the determinant that is output from the interactive device 1 but not selected by the user.

  The knowledge vector update unit 184 updates the user state information so as to increase the value of the element of the knowledge vector for one or more determination factors acquired by the device presentation term acquisition unit 182.

  Next, operation | movement of the dialogue apparatus 1 is demonstrated using the flowchart of FIG.

  (Step S201) The sentence output unit 17 outputs an initial sentence held in advance. The initial sentence is a sentence that is output to the user when the interactive apparatus 1 starts operation (for example, at the time of activation). The initial sentence is, for example, the sentence “Kyoto Tourist Information System. Recommend your favorite sightseeing spot”.

  (Step S202) The reception unit 14 determines whether a sentence is received from the user. If a sentence is accepted, the process goes to step S203, and if no sentence is accepted, the process returns to step S202.

  (Step S203) The voice recognition unit 142 of the reception unit 14 recognizes the sentence received in Step S202, and acquires a sentence of a character string (a sentence of a character code string).

  (Step S204) The reception unit 14 or a unit (not shown) determines whether or not the sentence received by the reception unit 14 satisfies an end condition. If the end condition is satisfied, the process ends. If the end condition is not satisfied, the process goes to step S205. The termination condition is, for example, that the user input sentence includes a sentence that matches a predetermined sentence pattern. Examples of the sentence pattern determined in advance include “go to <spot>” and “determine <spot>”.

  (Step S205) The score calculation unit 15 calculates two or more scores for two or more information recommendation methods. Details of the score calculation process will be described with reference to the flowchart of FIG.

  (Step S206) The sentence composition unit 16 composes one or more sentences to be output. Details of the sentence composition process will be described with reference to the flowchart of FIG.

  (Step S207) The sentence output unit 17 outputs one or more sentences configured by the sentence constructing unit 16.

  (Step S208) The user status information update unit 18 performs a user status information update process. Details of the user status information update processing will be described with reference to the flowchart of FIG.

  In the flowchart of FIG. 2, the sentence output unit 17 may output a predetermined sentence or a sentence composed of a predetermined sentence pattern before the end of the process.

  Next, details of the score calculation process in step S205 will be described with reference to the flowchart of FIG.

  (Step S <b> 301) The score calculation unit 15 reads user state information from the user state information storage unit 13.

  (Step S302) The score calculation unit 15 substitutes 1 for the counter i.

  (Step S303) The score calculation unit 15 determines whether or not the i-th information recommendation method exists in the information recommendation method storage unit 12. If the i-th information recommendation method exists, the process goes to step S304, and if it does not exist, the process returns to the upper process.

  (Step S304) The score calculation unit 15 reads evaluation information included in the i-th information recommendation method.

  (Step S305) The score calculation unit 15 calculates the score of the i-th information recommendation method using the user state information read in step S301 and the evaluation information read in step S304, and calculates the score as the i-th information. Temporary storage in association with the information recommendation method. For example, the score calculation unit 15 multiplies the user state information read in step S301 and the evaluation information read in step S304 to calculate a score.

  (Step S306) The score calculation unit 15 increments the counter i by 1. The process returns to step S303.

  In the flowchart of FIG. 3, the score calculation method of the score calculation unit 15 does not matter.

  Next, details of the sentence composition processing in step S206 will be described using the flowchart of FIG.

  (Step S401) The sentence composing unit 16 performs natural language processing on the immediately preceding user input sentence or / and the immediately preceding output sentence of the interactive device 1 to obtain a spot. For example, the sentence constructing unit 16 performs morphological analysis on the immediately preceding user input sentence or / and the immediately preceding output sentence of the dialogue apparatus 1 to acquire an independent word, searches the knowledge base 11 using the independent word as a key, A spot existing in the base 11 is acquired. However, here, there may be a case where the spot cannot be acquired.

  (Step S402) The sentence composing unit 16 performs natural language processing on the immediately preceding user input sentence or / and the immediately preceding output sentence of the interactive device 1, and acquires a determination factor. For example, the sentence constructing unit 16 performs morphological analysis on the immediately preceding user input sentence or / and the immediately preceding output sentence of the dialogue apparatus 1 to acquire an independent word, searches the knowledge base 11 using the independent word as a key, The determination factor existing in the base 11 is acquired. However, here, there may be a case where the determination factor cannot be acquired.

  (Step S403) The sentence structure part 16 judges whether the spot was able to be acquired by step S401. If it can be acquired, go to step S404, and if not, go to step S405.

  (Step S404) The sentence structure part 16 substitutes the spot acquired by step S401 to the variable "spot of interest". Note that the value of the variable “spot of interest” is a spot that is currently focused on in the dialogue. The value of the variable “spot of interest” is usually one spot.

  (Step S405) The sentence structure part 16 judges whether the decision factor was able to be acquired by step S402. If it can be acquired, the process goes to step S406, and if it cannot be acquired, the process goes to step S407.

  (Step S406) The sentence structure part 16 substitutes the determination factor acquired by step S402 to the variable "focused determination factor". Note that the value of the variable “focused determinant” is a determinant currently focused on in the dialogue. In addition, the value of the variable “target decision factor” may be two or more decision factors.

  (Step S407) The sentence composition unit 16 searches the knowledge base 11 using the value of the variable “focus spot” and the value of the variable “focus determination factor”, and provides an explanatory sentence corresponding to the focus spot and the focus determination factor. Read from the knowledge base 11. This explanatory text is an answer text to the input text from the user. Normally, the sentence composing unit 16 reads the value of the variable “focus spot” and the explanatory text corresponding to the variable “focus determination factor” from the knowledge base 11.

  (Step S408) The sentence structure part 16 performs the recommendation sentence acquisition process. The recommended sentence acquisition process will be described with reference to the flowchart of FIG.

  In the flowchart of FIG. 4, an answer sentence and a recommendation sentence are acquired. However, in the flowchart of FIG. 4, various sentence acquisition processes are conceivable, such as acquiring only a recommended sentence, acquiring an answer sentence, a recommended sentence, and other sentences.

  Next, the recommendation sentence acquisition process of step S408 is demonstrated using the flowchart of FIG.

  (Step S501) The sentence pattern information acquisition unit 161 of the sentence constructing unit 16 recommends the sentence pattern information included in one information recommendation method corresponding to the largest score among the two or more scores calculated by the score calculating unit 15 as information recommendation. Obtained from the technique storage unit 12.

  (Step S502) The variable value acquisition unit 162 substitutes 1 for the counter i.

  (Step S503) The variable value acquisition unit 162 determines whether or not the i-th variable exists in the sentence pattern information acquired in step S501. If it exists, go to step S504, and if not, go to step S508.

  (Step S504) The variable value acquisition unit 162 acquires the i-th variable in the sentence pattern information acquired in step S501. This variable also holds information on where to obtain the variable value from.

  (Step S505) The variable value acquisition unit 162 acquires one or more terms to be substituted into the i-th variable. This term is usually a spot or a determinant (may be a word specifying the determinant).

  (Step S506) The sentence composing means 163 substitutes one or more terms acquired in step S504 for the i-th variable in the sentence pattern information.

  (Step S507) The variable value acquisition unit 162 increments the counter i by one.

  (Step S508) The sentence composing means 163 changes the acquired sentence to a natural sentence. Return to upper process. If it is not necessary to change to a natural sentence, nothing is processed in step S508. Moreover, since the technique for changing a sentence to a natural sentence is a known technique, detailed description thereof is omitted. As a technique for changing a sentence to a natural sentence, for example, a statistical-based method is used.

  In the flowchart of FIG. 5, the process of step S508 may not be performed.

  Next, details of the user status information update processing in step S208 will be described using the flowchart of FIG.

  (Step S601) The user presentation term acquisition unit 181 of the user state information update unit 18 acquires one or more determinants from the latest (immediately preceding) sentence received by the reception unit 14. When one or more determination factors cannot be acquired from the sentence received immediately before, the user-presented term acquisition unit 181 acquires the focus determination factor. Further, the user-presented term acquisition unit 181 acquires one or more spots from the latest (immediately preceding) sentence received by the receiving unit 14. Then, the user-presented term acquisition unit 181 temporarily stores the acquired determination factor or / and the acquired spot in the buffer.

  (Step S602) The preference vector update unit 183 reads the preference vector included in the user status information in the user status information storage unit 13. Then, the preference vector update unit 183 updates the preference vector included in the user state information so that the value of the element corresponding to the determination factor acquired in step S601 is increased.

  (Step S603) The device presentation term acquisition unit 182 acquires one or more determinants from the latest (immediately preceding) sentence output by the sentence output unit 17. Further, the device presentation term acquisition unit 182 acquires one or more spots from the latest (immediately preceding) sentence output by the sentence output unit 17. Then, the device presentation term acquisition unit 182 temporarily stores the acquired determination factor or / and the acquired spot in the buffer.

  (Step S604) The knowledge vector update means 184 reads the knowledge vector possessed by the user state information in the user state information storage unit 13. The knowledge vector update unit 184 updates the knowledge vector included in the user state information so that the value of the element corresponding to the determination factor acquired in step S603 is increased.

  (Step S605) The user status information update unit 18 reads the number of conversation turns included in the user status information in the user status information storage unit 13. The number of dialogue turns is a logarithm of dialogue repeated. Then, the user status information update unit 18 updates the user status information in the user status information storage unit 13 with a value obtained by adding 1 to the read number of turns as a new number of turns.

  (Step S606) The user state information update unit 18 updates the immediately preceding user utterance action information. The immediately preceding user utterance action information is information relating to the sentence received by the receiving unit 14 immediately before, and is information corresponding to the type of information requested by the user (spot only, determinant name only, or both).

  (Step S607) The user state information update unit 18 updates the immediately preceding system utterance action information. The immediately preceding system utterance action information is information related to the sentence output by the sentence output unit 17 immediately before, and is information for specifying the selected information recommendation method.

  (Step S608) The user state information update unit 18 updates the system presentation history information. The system presentation history information is the number of spots and determination factors output by the interactive device 1 (system). The user status information update unit 18 uniquely processes the determination factor and the spot written in the buffer in step S603, and acquires the number of determination factors and the number of spots in the buffer. Then, the user status information update unit 18 acquires the number of determining factors and the number of spots in the buffer as system presentation history information.

  In the flowchart of FIG. 6, the information updated in steps S605 to S608 is an example of information constituting the user status information, and other information may constitute the user status information.

  Hereinafter, a specific operation of the interactive apparatus 1 in the present embodiment will be described. FIG. 7 is a conceptual diagram of the dialogue apparatus 1. In this specific example, the dialogue apparatus 1 is a system that supports the decision making of the user's visit destination while promoting the dialogue with the user, and provides sightseeing guidance in Kyoto.

  FIG. 8 is an example of a spot information management table held by the knowledge base 11. The spot information management table stores two or more pieces of spot information, which is information regarding spots. The spot here is a sightseeing spot in Kyoto. In this specification, a spot may be information that identifies a spot (which may be referred to as a spot identifier) or a concept of a spot. The spot information includes “spot”, “determining factor information”, “evaluation value”, and “description”. The “determining factor information” includes a “determining factor identifier” that identifies a determinant and a “determining factor” that is a phrase that accurately indicates the determinant (including a positive or negative term). Also, in this specific example, the determinants are “Garden is famous”, “Not crowded”, “World heritage”, “Scenic view”, “Good access”, “Colorful leaves”, “Sakura is famous”, “History in history”, “ Suppose that there are 10 factors (in no particular order): “Walking” and “There is an event”. The “evaluation value” is information indicating an evaluation with respect to the determinant of each spot. Here, each spot has “1” when it has the characteristic of the determinant indicated by the determinant information. In this case, a value of “0” is taken. The “evaluation value” may be a multi-level evaluation (for example, “1” to “5”) instead of the binary values “1” and “0”.

  FIG. 9 is an example of an information recommendation method management table in the information recommendation method storage unit 12. The information recommendation method management table stores six information recommendation methods. The information recommendation method includes “ID”, “method identifier”, “sentence pattern information”, and “evaluation information”. “ID” is a numerical value for identifying an information recommendation method. The “method identifier” is a character string that identifies the information recommendation method and indicates its significance. The “sentence pattern information” is information that constitutes a recommended sentence of information, or a recommended sentence of information (ID = 3 only). In the recommendation sentence of information, the tag (information of the beginning “<”, end “>”) is a variable. Also, information enclosed by “{” and “}” in the sentence pattern information is information (hereinafter referred to as an acquisition operation description) indicating a method for acquiring the value of the immediately preceding variable (acquisition operation). The current target spot is substituted for <target spot>. The current focus determination factor is substituted into <focus determination factor>. For <one or more undecided determinants>, a determinant that has not appeared in the previous dialogue for the spot of interest and whose evaluation value of the spot of interest is “1” is substituted. For <1 or more unspotted spots>, a spot whose attention determining factor has not appeared in the previous dialogue and whose evaluation value of the attention determining factor is “1” is substituted. <Highest favorite spot> is a spot that becomes the highest point when the rank (preference value) of each spot is calculated based on the preference vector of the user status information. The acquisition operation description “{decision factor equal to or less than 3 select where in the knowledge vector has the lowest value}” indicates an operation of acquiring a decision factor of 3 or less in the descending order of the value of the decision element in the knowledge vector. Since this acquisition operation description is attached to <one or more undecided determinants>, it indicates an operation of acquiring three or less undecided determinants in descending order of the value of the determinant element in the knowledge vector. It will be. The acquisition operation description “{decision factor equal to or smaller than select 3 where preference value in order of descending value}” indicates an operation of acquiring a determinant of 3 or less in descending order of value in the preference vector. Since this acquisition operation description is attached to <one or more undecided determinants>, it indicates an operation for acquiring three or less undecided determinants in descending order of the value in the preference vector. . The acquisition operation description “{select highest preference spot where the preference value is the maximum}” indicates that a spot estimated by the system to be the maximum preference value when viewed from the user is obtained from the current user status information. Show. For example, this acquisition operation description substitutes the preference vector and the evaluation value of each determinant for each spot into the arithmetic expression for calculating the preference value, calculates the preference value, and acquires the spot having the maximum preference value. Show. “Evaluation information” is information held for each information recommendation method. Then, using the current user status information and the evaluation information of the information recommendation method, the score of each information recommendation method is calculated, and using the sentence pattern information corresponding to the information recommendation method with the maximum score, the recommended sentence Is configured. For example, the score of the information recommendation method is calculated by multiplying the current user state information (vector) and the evaluation information (vector) of the information recommendation method.

In addition, it is assumed that the user status information storage unit 13 stores user status information described below. For example, it is assumed that the user state information has three elements: a knowledge vector “K _user ”, a preference vector “P _user ”, and a local weight matrix “V _user ”. Here, for the sake of simplicity, the elements of the _user preference vector “P _user = (p ₁ , p ₂ ,..., P _M )” are parameters consisting of binary values of “1” or “0”. And That is, the user is interested in is determinant m (or potentially interested), p _m is assumed to take the "1" when emphasized in determining spot. In addition, in order to express a state in which the user has a potential preference (the user himself / herself is unaware), the user's knowledge vector “K _user = (k ₁ , k ₂ ,..., K _M ) Is introduced. When the user knows that the system (interactive device 1) can handle the determining factor m, or when the system recommends the determining factor m, the vector element k _M takes “1”. By using these vectors, for example, determinants m is is a factor that the user has potentially interesting, the state that the user is not aware of it _{(k m = 0, p m} = 1) Can be expressed as Further, assuming that the user determines only the local weight v _nm for the spot n from the viewpoint of the user's determinant m from the information presented from the system, the system uses “ID = “1” is assumed when the user is informed of the evaluation of the spot using any one of the recommended methods “1, 2, 6”.

The user state information here corresponds to a feature vector of the dialog state. More specifically, here, the user status information includes the following six types of information. The first is the number of turns. Here, the number of turns is expressed by five parameters using a sawtooth function. The second is immediately preceding user utterance action information. The immediately preceding user utterance action information is, for example, a vector (x _i , x _{i + 1} , x _{i + 2} , x _{i + 3} , x _{i + 4} ), and (1 if a _user ^t−1 = x _i , otherwise 0). Here, “a _user ^t−1 ” is the utterance of the previous user. In addition, when the utterance of the previous user includes only a spot (which may be referred to as a spot name) recommended by the system (dialogue device 1) or a determinant, “x _i = 1” and other vector elements Is “0”. When a spot not recommended by the system is included, “x _{i + 1} = 1” and the elements of other vectors are “0”. When only the determinants not recommended by the system are included, “x _{i + 2} = 1” and the elements of the other vectors are “0”. Further, when both spots and determinants not recommended by the system are included, “x _{i + 3} = 1” and the elements of other vectors are “0”. Further, when none of these is included, “x _{i + 4} = 1” and the elements of the other vectors are “0”. The third is the immediately preceding system utterance action information. The immediately preceding system utterance action information is, for example, a vector (y _i , y _{i + 1} , y _{i + 2} , y _{i + 3} , y _{i + 4} , y _{i + 5} , y _{i + 6} ), and (1 if a _sys ^t-1 = y _i , otherwise 0) Here, “a _sys ^t-1 ” is the utterance of the immediately preceding system. Further, when the information recommendation method 1 is used as the utterance of the immediately preceding system, “y _{i + 1} = 1” and the elements of the other vectors are “0”. When the information recommendation method 2 is used, “y _{i + 2} = 1”, and the elements of the other vectors are “0”. That is, when the information recommendation method n is used, “y _{i + n} = 1”, and the elements of other vectors are “0”. The fourth is a knowledge vector for a user's determinant. Note that the knowledge vector may be calculated by “Σ _{n = 1} ^N Pr (k _n = 1)”, which is the knowledge of the user's decision factor. Here, k _n is the element values of the knowledge vector for the n th determinant. Pr (k = 1) is a posterior probability (certainty factor) estimated by the system that “k is 1”. Here, it is assumed that the element value of the preference vector is “1” or “0”. The fifth is the number of spots and determinants presented by the system. That is, the fifth item is system presentation history information, for example, “Σ _{n = 1} ^N Σ _{m = 1} ^M v _nm ”. Here, v _nm is an evaluation value for the determinant n of the spot m that the interactive apparatus 1 has. The sixth is a preference vector. The preference vector is information indicating a user's preference for each determining factor. Note that the preference vector may be replaced with an expected value of the probability that the user attaches importance to the determination factor. This expected value is indicated by “Pr (k _n = 1) × Pr (p _n = 1))”, and has 10 parameters in total for each decision factor. Here, “p _n ” is an element value of the preference vector. Here, it is assumed that the element value of the preference vector is “1” or “0”.

  The user state information stored in the user state information storage unit 13 is a vector having the element values described above, and is a vector (s1, s2,..., S29) having 29 element values. , And. The initial value of the user state information is (s0001, s0002,..., S0029).

  Further, it is assumed that the sentence output unit 17 stores an initial sentence “Kyoto sightseeing system. Recommend your favorite sightseeing spot”, which is a sentence that is output when the interactive device 1 is activated.

  Further, it is assumed that the reception unit 14 holds the sentence patterns “go to <spot>” and “determined to <spot>”, which are conditions for ending the conversation. Here, <spot> is a variable to which a spot can be substituted.

  In this state, it is assumed that the user has activated the interactive device 1. Next, the sentence output unit 17 reads the initial sentence and outputs the initial sentence “Kyoto sightseeing system. Recommend your favorite sightseeing spot” by voice.

  Next, it is assumed that the user inputs a voice message “Tell me about cherry blossoms at Ninna-ji Temple”. Then, the voice reception means 141 of the reception unit 14 receives the voice “Tell me about cherry blossoms at Ninnaji.” Next, the voice recognition means 142 recognizes this voice and acquires the sentence “Tell me about cherry blossoms at Ninna-ji.”

  Next, the reception unit 14 changes to the sentence pattern “I will go to <spot>” or “I decided to <spot>” with the input sentence “Tell me about cherry blossoms at Ninna-ji.” Judge that they do not match.

  Next, the score calculation unit 15 calculates six scores for each information recommendation method. That is, first, the score calculation unit 15 reads the user state information (s0001, s0002,..., S0029) from the user state information storage unit 13.

  Next, the score calculation unit 15 includes evaluation information (vector) included in each of the first to sixth information recommendation methods and user state information (s0001, s0002,...) In the information recommendation method storage unit 12. , s0029) for each information recommendation method. And the score calculation part 15 calculates the score of six information recommendation methods.

  Next, the sentence composition unit 16 performs natural language processing on the immediately preceding user input sentence, and acquires the spot “Ninna-ji”. In addition, the sentence composing unit 16 performs natural language processing on the immediately preceding user input sentence and acquires the determination factor “sakura”. For example, the sentence composition unit 16 performs morphological analysis on the user input sentence, and acquires the independent words “Ninnaji”, “Sakura”, and “Teach me”. Then, the sentence composition unit 16 searches the knowledge base 11 using the three independent words as keys, detects that “Ninna-ji” is a spot, and “Cherry blossom” is the determining factor. Get the factor “Sakura”.

  Next, the sentence composition unit 16 substitutes the acquired spot “Ninna-ji” for the variable “spot of interest”. Moreover, the sentence structure part 16 substitutes the acquired determination factor "cherry blossom" for the variable "focused determination factor".

  Next, the sentence composition unit 16 searches the knowledge base 11 using the value “Ninna-ji” of the variable “focused spot” and the value “cherry blossom” of the variable “focused determination factor” to find the focused spot and the focused determination factor. Read from the knowledge base 11 "Omuro cherry tree is a rare cherry tree with a low tree height and a single fragrant white flower that blooms from the root. This explanatory text becomes an answer text.

  Next, the sentence composition unit 16 performs a recommendation sentence acquisition process. That is, first, the sentence pattern information acquisition unit 161 of the sentence composition unit 16 uses one information recommendation method corresponding to the largest score among the six scores calculated by the score calculation unit 15 (here, a method of ID = 1). Is acquired from the information recommendation method management table of FIG. 9. The sentence pattern information “<the spot of interest> is <one or more undecided determinants>.

  Next, the variable value acquisition unit 162 acquires the first variable <target spot> from the sentence pattern information. Next, the variable value acquiring unit 162 acquires one or more terms (the value “Ninnaji” of the variable “target spot”) that is substituted into the variable <target spot>. Next, the sentence composing means 163 substitutes the acquired one or more terms “Ninna-ji” into the position of the first variable in the sentence pattern information. > Would you explain something?

  Next, the variable value acquisition unit 162 acquires the second variable <one or more undecided determination factors> from the sentence pattern information. Then, the variable value acquisition unit 162 acquires a variable <one or more undecided determination factors>. That is, the variable value acquisition unit 162 has the evaluation value of the value “Ninna-ji” of the variable “spot of interest” as “1”, and the determinants other than the previously determined determinant “cherry blossom” are “scenery is good” Is famous, "Colorful leaves are famous", "World Heritage", "Event is". Then, the sentence composing means 163 substitutes the acquired one or more terms into the second variable location in the sentence pattern information, and says, “Ninna-ji Temple has a good view, garden is famous, and autumn leaves There are famous, world heritage, and events. Next, the sentence composing means 163 is a natural sentence "Ninna-ji Temple has a good view, gardens are famous, autumn leaves are famous, there are world heritage, and events. The view is good, the garden is famous for its autumn leaves, it is a World Heritage site, and there are events.

In other words, the above-described processing is executed as “Method 1 ((Spot _Ninnaji ), (Det _scenery , Det _garden , Det _{autumn leaves} , Det _{world heritage} , Det _event ))”. Method1 () means that the information recommendation method 1 is applied. “Spot _Ninna-ji ” is the spot “Ninna-ji”, “Det _Landscape ” is the determinant “Scenery”, “Det _Garden ” is the determinant “Garden”, “Det Colored _Leaves ” is the determinant “Autumn Leaves”, “Det _{World Heritage} ” Means determinant “world heritage”, “Det _event ” means determinant “event”.

  Next, the sentence output unit 17 outputs an answer sentence “Omuro Sakura is a rare cherry tree with a low tree height and a single fragrant white flower that blooms from the root. The flowering time is late and decorates the end of spring in Kyoto.” . Subsequently, the sentence output unit 17 outputs a recommendation sentence “Ninna-ji Temple has a good view, is famous for its gardens and autumn leaves, is a World Heritage site, and has an event.

  Next, the user status information update unit 18 performs user status information update processing as follows. That is, first, the user presentation term acquisition unit 181 of the user state information update unit 18 acquires one or more determinants “cherry blossom” from the latest sentence “Tell me about cherry blossoms at Ninna-ji Temple” received by the reception unit 14. To do.

  And the preference vector update means 183 reads the preference vector which the user state information of the user state information storage part 13 has. Then, the preference vector update unit 183 updates the preference vector included in the user state information so that the value of the element corresponding to the acquired determination factor “Sakura” becomes large. It does not matter how large the value of the element corresponding to the determinant factor “sakura” is. The value to be increased may be a fixed value, a fixed ratio, or dynamically change.

  Next, the device presentation term acquisition means 182 uses the latest recommended sentence output from the sentence output unit 17 “Ninna-ji Temple has a good view, is famous for its garden, autumn leaves, is a World Heritage site, and has an event. Get one or more determinants “scenery” “garden” “autumn leaves” “world heritage” “event” from

  Next, the knowledge vector update unit 184 reads the knowledge vector included in the user state information in the user state information storage unit 13. The knowledge vector update means 184 updates the knowledge vector included in the user state information so that the value of the element corresponding to the acquired determinants “scenery”, “garden”, “autumn leaves”, “world heritage”, “event” becomes large. . It does not matter how much the value of the element corresponding to the determinant “scenery” or the like is increased. The value to be increased may be a fixed value, a fixed ratio, or dynamically change.

  Next, the user state information update unit 18 reads the number of conversation turns “0” included in the user state information in the user state information storage unit 13. Then, the user state information update unit 18 updates the user state information in the user state information storage unit 13 with the value “1” obtained by adding 1 to the read turn number “0” as the new turn number.

  Next, the user state information update unit 18 updates the immediately preceding user utterance action information from “0” to “1”.

  Next, the user status information update unit 18 updates the immediately preceding system utterance action information from “0” to “1”.

  Next, the user status information update unit 18 updates the number of spots constituting the system presentation history information from “0” to “1” (since “Ninna-ji” was output). In addition, the user status information update unit 18 changes the number of determinants constituting the system presentation history information from “0” to “5” (because “landscape”, “garden”, “autumn leaves”, “world heritage”, and “event” are output). Update to

  As described above, the user status information is updated to the latest value (s0101, s0102,..., S0129).

  Next, it is assumed that the dialog between the user and the dialog device 1 is performed several times, and the fifth utterance “How is the scenery here?” Is inputted by voice. The current spot of interest is “Ninna-ji”.

  Next, the voice receiving means 141 of the receiving unit 14 receives a voice sentence “How is the scenery here?” From the user.

  Next, the voice recognition unit 142 of the reception unit 14 recognizes the received sentence “How is the scenery here?” And acquires a sentence of a character string.

  Next, the reception unit 14 does not match the sentence pattern “going to <spot>” or “decided to <spot>” where the input sentence “How is the scenery here?” Is an end condition. Judge that.

  Next, the score calculation unit 15 calculates six scores for each of the six information recommendation methods in the same manner as described above.

  That is, first, the score calculation unit 15 reads the current user state information (s0501, s0502,..., S0529) from the user state information storage unit 13.

  Next, the score calculation unit 15 stores evaluation information (vector) and user status information (s0501, s0502,...) Included in the first to sixth information recommendation methods in the information recommendation method storage unit 12. , s0529) for each information recommendation method. And the score calculation part 15 calculates the score of six information recommendation methods.

  Next, the sentence constructing unit 16 performs natural language processing on the immediately preceding user input sentence and acquires the determination factor “scenery”. In addition, the sentence structure part 16 was not able to acquire a spot from a user input sentence here.

  Next, the sentence constructing unit 16 substitutes the acquired determination factor “scenery” for the variable “target determination factor”.

  Next, the sentence composition unit 16 searches the knowledge base 11 using the value “Ninna-ji” of the variable “focus spot” and the value “scenery” of the variable “focus determination factor”, and determines the focus spot and the focus determination factor. Read from the knowledge base 11 the explanation corresponding to “From the top of Sanmon. This explanatory text becomes an answer text.

  Next, the sentence composition unit 16 performs a recommendation sentence acquisition process. That is, first, the sentence pattern information acquisition unit 161 of the sentence composition unit 16 uses one information recommendation method corresponding to the largest score among the six scores calculated by the score calculation unit 15 (in this case, a method with ID = 2). 9 is acquired from the information recommendation method management table of FIG. 9. The sentence pattern information of “<focus determination factor> where <1 or more unspotted spots> etc. can be introduced” is introduced.

  Next, the variable value acquisition unit 162 acquires the first variable <focus determination factor> from the sentence pattern information. Next, the variable value acquisition unit 162 acquires one or more terms (the value “scenery” of the variable “focus determination factor”) that is substituted into the variable <focus determination factor>. Next, the sentence composing means 163 substitutes the acquired one or more terms “good scenery” into the position of the first variable in the sentence pattern information, and “if the scenery is good, <1 or more Unspotted spot> etc. can be introduced. In addition, when assigning a value to a variable related to “determination factor”, the variable value acquisition unit 162 acquires the attribute value “determination factor” in FIG.

  Next, the variable value acquisition unit 162 acquires the second variable <one or more unspotted spots> from the sentence pattern information. Then, the variable value acquisition unit 162 acquires the variable <one or more unspotted spots>. In other words, the variable value acquisition means 162 has a value “1” for the value “scenery” of the variable “focused determinant”, and spots “Kiyomizu-dera” and “Fushimi Inari-taisha” excluding the existing determinant “Ninna-ji” “Kuramaji” is acquired from the knowledge base 11 (FIG. 8). Next, the sentence composing means 163 substitutes the acquired terms “Kiyomizu-dera”, “Fushimi Inari-taisha” and “Kurama-ji” for the second variable in the sentence pattern information. Kiyomizu Temple, Fushimi Inari Taisha Shrine, Kurama Temple, etc. ”can be introduced. Next, the sentence composing means 163 tries to convert “If the scenery is good, you can introduce Kiyomizu Temple, Fushimi Inari Taisha, Kurama Temple, etc.” to natural sentences, but there is no need to change and recommend Acquired the sentence “If you have a good view, you can introduce Kiyomizu Temple, Fushimi Inari Taisha Shrine, Kurama Temple, etc.”

In other words, the above-described processing is executed “Method 2 ((Spot _{Kiyomizu Temple} , Spot _{Fushimi Inari Taisha} , Spot _{Kurama Temple} ), (Det _Landscape ))”. Method2 () means that the information recommendation method 2 is applied. “Spot _{Kiyomizu-dera} ” etc. means the spot “Kiyomizu-dera” etc.

  Next, the sentence output unit 17 outputs the answer sentence “From the top of Sanmon. Subsequently, the sentence output unit 17 outputs a recommendation sentence “If the scenery is good, you can introduce Kiyomizu Temple, Fushimi Inari Taisha, Kurama Temple, etc.”.

  Next, the user status information update unit 18 performs user status information update processing as follows. That is, first, the user presentation term acquisition unit 181 of the user status information update unit 18 acquires one or more determinants “scenery” from the latest sentence “how is the scenery here” received by the reception unit 14.

  And the preference vector update means 183 reads the preference vector which the user state information of the user state information storage part 13 has. Then, the preference vector updating unit 183 updates the preference vector included in the user state information so that the value of the element corresponding to the acquired determination factor “scenery” becomes large. It does not matter how large the value of the element corresponding to the determining factor “scenery” is.

  Next, the device presentation term acquisition means 182 has one or more of the latest recommended sentences output by the sentence output unit 17 “If the scenery is good, Kiyomizu Temple, Fushimi Inari Taisha, Kurama Temple, etc. can be introduced.” Get the determinant "scenery".

  Next, the knowledge vector update unit 184 reads the knowledge vector included in the user state information in the user state information storage unit 13. The knowledge vector update unit 184 updates the knowledge vector included in the user state information so that the value of the element corresponding to the acquired determination factor “scenery” becomes large. For example, when the value of the element corresponding to the determination factor “scenery” is already the maximum value, the element value does not change.

  Next, the user state information update unit 18 reads the number of conversation turns “5” included in the user state information in the user state information storage unit 13. Then, the user state information update unit 18 updates the user state information in the user state information storage unit 13 with the value “6” obtained by adding 1 to the read turn number “5” as the new turn number.

  Next, the user status information update unit 18 keeps the previous user utterance action information as “1”. Further, the user status information update unit 18 keeps the previous system utterance action information as “1”.

  Next, the user status information update unit 18 updates the number of spots constituting the system presentation history information from “1” to “4” (since “Kiyomizu Temple”, “Fushimi Inari Taisha” and “Kurama Temple” are output). Since “scenery” has already appeared, the user state information update unit 18 keeps the number of determining factors constituting the system presentation history information as “5”.

  As described above, the user status information is updated to the latest value (s0601, s0602,..., S0629).

  Next, it is assumed that the user hears the answer sentence and the recommendation sentence output by the dialogue apparatus 1 and answers “Kiyomizu Temple” (sixth utterance).

  Next, the voice reception unit 141 of the reception unit 14 receives a voice sentence “Kiyomizu Temple” from the user.

  Next, the voice recognition unit 142 of the reception unit 14 recognizes the received sentence “Kiyomizu-dera” and acquires a sentence of a character string.

  Next, the reception unit 14 determines that the input sentence “Kiyomizu-dera” does not match the sentence pattern “go to <spot>” or “determined to <spot>” as the end condition.

  Next, the score calculation unit 15 calculates six scores for each of the six information recommendation methods in the same manner as described above.

  That is, first, the score calculation unit 15 reads the current user status information (s0601, s0602,..., S0629) from the user status information storage unit 13.

  Next, the score calculation unit 15 includes evaluation information (vector) and user state information (s0601, s0602,...) Included in each of the first to sixth information recommendation methods in the information recommendation method storage unit 12. , s0629) for each information recommendation method. And the score calculation part 15 calculates the score of six information recommendation methods.

  Next, the sentence composing unit 16 performs natural language processing on the immediately preceding user input sentence, and acquires the spot “Kiyomizu Temple”. In addition, the sentence structure part 16 was not able to acquire a decision factor from a user input sentence here.

  Next, the sentence composition unit 16 substitutes the acquired spot “Kiyomizu-dera” for the variable “target spot”. Note that the current variable “Focus Determination Factor” is “Scenery”.

  Next, the sentence composition unit 16 searches the knowledge base 11 using the value “Kiyomizu-dera” of the variable “focused spot” and the value “scenery” of the variable “focused determination factor”, and sets the focused spot and the focused determination factor as the focused spot. The corresponding explanation “The stage of Shimizu is built on a slope and the city view from here is wonderful” is read from the knowledge base 11. This explanatory text becomes an answer text.

  Next, the sentence composition unit 16 performs a recommendation sentence acquisition process. That is, first, the sentence pattern information acquisition unit 161 of the sentence composition unit 16 uses one information recommendation method corresponding to the largest score among the six scores calculated by the score calculation unit 15 (here, the method of ID = 4). The sentence pattern information possessed by “is able to explain other things such as <1 or more undecided determinants> {determining factors of select 3 or less where where the value in the knowledge vector is low} etc.”, etc. Obtain from the recommended method management table.

  Next, the variable value acquisition unit 162 acquires the first variable <one or more undecided determination factors> from the sentence pattern information. Next, the variable value acquisition unit 162 determines one or more terms to be assigned to the variable <one or more undecided determinants> according to the acquisition operation description {decision factor of select 3 or less where value in the knowledge vector is low}. (The value “scenery” of the variable “focus determination factor”) is acquired. That is, the variable value acquisition means 162 has three of the determination factors “congestion”, “world heritage”, “walking”, etc. other than the above-described determination factors “cherry blossom” and “scenery” in order of increasing values in the knowledge vector. (Here, “World Heritage” “Walk” “History”). Next, the sentence composing means 163 substitutes the acquired three determinants “world heritage”, “walk” and “history” in the first variable part in the sentence pattern information, and “other world heritage, Can explain the famous places that can be walked in history. "

In other words, the above-mentioned process, so that you run the "Method4 ((NULL), (Det _{World Heritage Site,} Det _walk, Det _history))". Method4 () means that the information recommendation method 4 is applied. “Det _{World Heritage} ” etc. means determinant “World Heritage” etc.

  Next, the sentence output unit 17 outputs an answer sentence “The stage of Shimizu is built on a slope and the scenery of the city you want from here is wonderful”. Subsequently, the sentence output unit 17 outputs a recommendation sentence “I can explain other world heritage sites, walkable places, famous places in history, etc.”.

  Next, it is assumed that the user state information update unit 18 performs the user state information update process in the same manner as described above, and the user state information is updated to the latest value (s0701, s0702,..., S0729).

  After that, it is assumed that the dialogue between the user and the dialogue apparatus 1 proceeds, and the user voice-inputs “I will go to Nanzenji” in the 16th utterance.

  Next, the voice reception means 141 of the reception unit 14 receives a voice sentence “I will go to Nanzenji” from the user.

  Next, the voice recognition unit 142 of the reception unit 14 recognizes the received sentence “I will go to Nanzenji” and acquires a sentence of a character string.

  Next, the reception unit 14 determines that the input sentence “I will go to Nanzenji” matches the sentence pattern “I will go to <spot>” which is the end condition. This is because the sentence “Let's go to Nanzenji” includes a sentence that matches the sentence pattern “Go to <spot>.”

  Then, the process ends. At the end of the process, the sentence output unit 17 determines a predetermined sentence “Thank you for using the Kyoto Tourist Information System” and a predetermined sentence pattern “<Spot>”. Do you have anything else you want to know? " In this case, the sentence output unit 17 decided from the sentence pattern “<Spot>. Do you have anything else to know?” From “Nanzenji Temple. Do you have anything else to know? ? "Is output.

  The flow of the above dialogue is shown in FIG. In FIG. 10, S [numerical value] indicates an utterance from the interactive apparatus 1 (system), and U [numeric value] indicates an utterance from the user. Ans indicates an answer sentence, and Rec indicates a recommendation sentence.

  As described above, according to the present embodiment, it is possible to appropriately support the user's decision making by performing the conversation while dynamically changing the information related to the user's knowledge and preferences according to the progress of the conversation with the user. .

  In addition, according to this Embodiment, the content of user state information does not ask | require. Further, it does not matter how the user status information is changed according to the progress of the dialogue with the user. However, a value (such as an element value constituting a preference vector) regarding a spot or a determinant included in the user's utterance and not a negative content is increased (selected by the dialogue apparatus 1). To make it easier). In addition, the value (such as element values constituting the knowledge vector) related to the knowledge about the spots and the determination factors included in the utterance output from the interactive device 1 is increased (to be easily selected by the interactive device 1).

Further, an example of changing the user state information by dialogue in the present embodiment will be described in detail below. The knowledge vector of the prior user state information is “Ksys = (0.22, 0.01, 0.02, 0.18,...)”, And the prior preference vector is “Psys = (0.37). , 0.19, 0.48, 0.38, ...) ". Then, it is assumed that the dialogue apparatus 1 has selected the information recommendation method 1 in response to the user's utterance. That is, it is assumed that the interactive apparatus 1 executes “a _sys = Method1 {(Spot ₅ ), (Det ₁ , Det ₃ , Det ₄ )}”. Then, it is assumed that the dialogue apparatus 1 has obtained a recommendation sentence “Regarding Ninnaji (Spot ₅ ), it can explain the garden (Det ₁ ), the World Heritage (Det ₃ ), and the autumn leaves (Det ₄ )”. Then, it is assumed that the user inputs “Tell me about World Heritage (Det ₃ )”.

  Then, the user state information update unit 18 uses the subsequent knowledge vector “Ksys = ((1.00, 0.01, 1.00, 1.00,...)” And the subsequent preference vector “Psys = (0 .26, 0.19, 0.65, 0.22, ...) ", and the vector is (garden, crowded, world heritage, autumn leaves, scenery, access, cherry blossom, history, walk, event) As the determinants “garden”, “world heritage”, and “autumn leaves” are output from the dialogue device 1 (system), the subsequent knowledge vector “garden” “world heritage” The element value of “Autumn leaves” has improved, and the element value of “World Heritage” in the preference vector has increased due to the input of the determinant “World Heritage” by the user. "By not selecting" Autumn leaves " Element values of the "garden", "autumn leaves" of good vector is reduced.

  Further, the processing in the present embodiment may be realized by software. Then, this software may be distributed by software download or the like. Further, this software may be recorded and distributed on a recording medium such as a CD-ROM. This also applies to other embodiments in this specification. Note that the software that implements the interactive apparatus 1 in the present embodiment is the following program. That is, this program has a spot having a spot, one or more determinants that are factors for determining the spot, and an evaluation value indicating evaluation of each of the one or more determinants of the spot on the storage medium. Used when selecting a knowledge base that stores two or more pieces of information, sentence pattern information that indicates a sentence pattern output by the interactive device or a sentence pattern output by the interactive device, and the sentence pattern information Two or more information recommendation methods having evaluation information of sentence pattern information to be performed; information indicating user status; preference vector which is information indicating user preference for one or more determination factors; and one or more information User status information having knowledge vectors indicating the user's knowledge for each determinant is stored, and the computer accepts a sentence input by the user And a score calculation unit that applies the user status information to evaluation information of each of the two or more information recommendation methods stored in the storage medium and calculates two or more scores for the two or more information recommendation methods And using the two or more scores calculated by the score calculation section, the sentence pattern information possessed by one information recommendation method is acquired, and a sentence composition section that composes a sentence from the sentence pattern information; and the sentence composition section Acquire at least one spot or one or more determinants from one or more sentences out of a sentence output part that outputs the composed sentence and a sentence received by the reception part or a sentence output by the sentence output part. The score calculation is a program that functions as a user status information update unit that updates user status information of the storage medium using the one or more spots or one or more determining factors. Applies the user status information updated by the user status information update unit to the evaluation information of each of the two or more information recommendation methods stored in the information recommendation method storage unit, and the two or more information recommendation methods This is a program that functions as one that calculates a score of 2 or more for.

  In the above program, the sentence composing unit includes sentence pattern information obtaining means for obtaining sentence pattern information included in one information recommendation method corresponding to the largest score among two or more scores calculated by the score calculating unit; , One or more variables included in the sentence pattern information acquired by the sentence pattern information acquisition unit, and a spot or determination factor corresponding to the variable, the sentence output by the sentence output unit immediately before, or the reception unit The variable value acquisition unit that acquires from one or more of the sentences received immediately before, and the term acquired by the variable value acquisition unit in the variable part of the sentence pattern information acquired by the sentence pattern information acquisition unit It is preferable to make a computer function as what comprises the sentence structure means which inserts and comprises a sentence.

  In the above program, the variable value acquisition unit acquires one or more variables included in the sentence pattern information acquired by the sentence pattern information acquisition unit, and is one or more spots or 1 that are candidates corresponding to the variable. The above determinants are obtained from one or more sentences of the sentence output by the sentence output unit or the sentence received by the reception unit immediately before, and are one or more spots or one or more candidates as the candidates. Let the computer function as one that selects a spot or a determinant corresponding to the variable using one or more spots as candidates or an evaluation value of the knowledge base corresponding to one or more determinants from the determinants That is preferred.

  In the above program, the user status information update unit includes a user presentation term acquisition unit that acquires at least one or more determinants from the sentence received by the reception unit, and one of the sentences output by the sentence output unit. From the above sentence, the device presentation term acquisition means for acquiring at least one or more determination factors, and the preference for updating the preference vector element value for the one or more determination factors acquired by the user presentation term acquisition means. It is possible to cause a computer to function as comprising a vector updating unit and a knowledge vector updating unit that updates the value of an element of a knowledge vector for one or more determinants acquired by the device presentation term acquisition unit. Is preferred.

  In the above program, the reception unit includes a voice reception unit that receives a voice input by a user, and a voice recognition unit that recognizes the voice and converts the voice into a character string. It is preferable to make a computer function as a voice output of a sentence formed by the component.

  FIG. 11 shows the external appearance of a computer that executes the program described in this specification to realize the interactive apparatus or the like of the above-described embodiment. The above-described embodiments can be realized by computer hardware and a computer program executed thereon. FIG. 11 is an overview diagram of the computer system 340, and FIG. 12 is a block diagram of the computer system 340.

  In FIG. 11, the computer system 340 includes a computer 341 including an FD drive and a CD-ROM drive, a keyboard 342, a mouse 343, a monitor 344, and a microphone 345.

  In FIG. 12, in addition to the FD drive 3411 and the CD-ROM drive 3412, the computer 341 stores an MPU 3413, a bus 3414 connected to the CD-ROM drive 3412 and the FD drive 3411, and a program such as a bootup program. A RAM 3416 for temporarily storing application program instructions and providing a temporary storage space; and a hard disk 3417 for storing application programs, system programs, and data. Although not shown here, the computer 341 may further include a network card that provides connection to the LAN.

  A program for causing the computer system 340 to execute the functions of the interactive apparatus or the like of the above-described embodiment is stored in the CD-ROM 3501 or FD 3502, inserted into the CD-ROM drive 3412 or FD drive 3411, and further stored in the hard disk 3417. May be forwarded. Alternatively, the program may be transmitted to the computer 341 via a network (not shown) and stored in the hard disk 3417. The program is loaded into the RAM 3416 at the time of execution. The program may be loaded directly from the CD-ROM 3501, the FD 3502, or the network.

  The program does not necessarily include an operating system (OS), a third-party program, or the like that causes the computer 341 to execute the functions of the interactive device according to the above-described embodiment. The program only needs to include an instruction portion that calls an appropriate function (module) in a controlled manner and obtains a desired result. How the computer system 340 operates is well known and will not be described in detail.

  Further, the computer that executes the program may be singular or plural. That is, centralized processing may be performed, or distributed processing may be performed.

  In each of the above embodiments, it is needless to say that two or more communication means (terminal information transmission unit, terminal information reception unit, etc.) existing in one device may be physically realized by one medium. .

In each of the above embodiments, each process (each function) may be realized by centralized processing by a single device (system), or by distributed processing by a plurality of devices. May be.
The present invention is not limited to the above-described embodiments, and various modifications are possible, and it goes without saying that these are also included in the scope of the present invention.

  As described above, the dialogue apparatus according to the present invention appropriately determines the user's decision by performing the dialogue while dynamically changing the information on the user's knowledge and preferences according to the progress of the dialogue with the user. This is useful as a tourist information system.

DESCRIPTION OF SYMBOLS 1 Dialogue device 11 Knowledge base 12 Information recommendation method storage part 13 User state information storage part 14 Reception part 15 Score calculation part 16 Sentence structure part 17 Sentence output part 18 User state information update part 141 Voice reception means 142 Voice recognition means 161 Sentence pattern 161 sentence pattern Information acquisition means 162 Variable value acquisition means 163 Sentence formation means 181 User presentation term acquisition means 182 Device presentation term acquisition means 183 Preference vector update means 184 Knowledge vector update means

Claims (11)

An interactive device that interacts with the user regarding spots,
Knowledge storing two or more pieces of spot information including a spot, one or more determinants that are factors for determining the spot, and an evaluation value indicating the evaluation of each of the one or more determinants of the spot Base and
2 or more having sentence pattern information that is information indicating a sentence output by the interactive device or a pattern of a sentence output by the interactive device, and evaluation information of sentence pattern information used when selecting the sentence pattern information An information recommendation method storage unit storing information recommendation methods of
Stores user state information including information indicating a user state and a preference vector which is information indicating a user's preference for one or more determinants and a knowledge vector indicating a user's knowledge for one or more determinants A user status information storage unit,
A reception unit for receiving a sentence input by the user;
Score calculation for calculating the two or more scores for each of the two or more information recommendation methods by applying the user status information to evaluation information of the two or more information recommendation methods stored in the information recommendation method storage unit And
Using the two or more scores calculated by the score calculation unit, acquiring sentence pattern information possessed by one information recommendation method, and constructing a sentence from the sentence pattern information;
A sentence output unit for outputting a sentence formed by the sentence constructing unit;
At least one spot or one or more determinants are obtained from one or more sentences of the sentence received by the reception unit or the sentence output unit, and the one or more spots or one or more A user state information update unit that updates user state information in the user state information storage unit using a determination factor;
The score calculation unit
The user status information updated by the user status information update unit is applied to evaluation information of each of the two or more information recommendation methods stored in the information recommendation method storage unit, and 2 for each of the two or more information recommendation methods. An interactive device that calculates the above score. The user status information is:
The dialogue apparatus according to claim 1, further comprising dialogue amount information that is information indicating an amount of dialogue between the dialogue device and the user up to the present time. The user status information is:
3. The information according to claim 1 or 2, comprising at least one of information related to a determinant corresponding to a sentence output by the interactive device immediately before or information related to a determinant corresponding to a sentence received from a user immediately before. Interactive device. The user status information is:
The interactive apparatus according to any one of claims 1 to 3, further comprising local weight information that is information on one or more local weights indicating the importance of the spot from the viewpoint of a user's determinant. The information recommendation method storage unit includes:
Current spot information recommendation method having sentence pattern information for recommending detailed explanation and evaluation information of the sentence pattern information for the spot explained immediately before, recommending another tourist spot related to the determinant explained immediately before Current topic determinant information recommendation method having sentence pattern information and evaluation information of the sentence pattern information, and an open prompt having sentence pattern information indicating an open prompt without recommending any information and evaluation information of the sentence pattern information 5. The dialogue apparatus according to claim 1, wherein at least an information recommendation method is stored. The sentence component is
Sentence pattern information acquisition means for acquiring sentence pattern information possessed by one information recommendation method corresponding to the largest score among the two or more scores calculated by the score calculation unit;
One or more variables included in the sentence pattern information acquired by the sentence pattern information acquisition unit are acquired, and a spot or determination factor corresponding to the variable is immediately output by the sentence output unit or the reception unit Variable value acquisition means for acquiring from one or more of the sentences received immediately before;
The sentence composition means which comprises the sentence which inserts the term which the variable value acquisition means acquired in the location of the variable of sentence pattern information which the sentence pattern information acquisition means acquired, and constitutes a sentence. Or the interactive device described. The variable value acquisition means includes
The sentence output unit acquires one or more variables included in the sentence pattern information acquired by the sentence pattern information acquisition unit, and the sentence output unit immediately displays one or more spots or one or more determinants as candidates corresponding to the variables. One or more spots that are acquired from one or more spots or one or more determinants of the candidate obtained from one or more sentences of the sentence that has been output or the sentence that the reception unit has received immediately before The interactive apparatus according to claim 6, wherein a spot or a determinant corresponding to the variable is selected using an evaluation value of the knowledge base corresponding to one or more determinants. The user status information update unit
User presented term acquisition means for acquiring at least one or more determinants from the sentence received by the reception unit;
Device presentation term acquisition means for acquiring at least one or more determinants from one or more sentences of the sentences output by the sentence output unit;
Preference vector update means for updating the user-presented term acquisition means so as to increase the value of the element of the preference vector for the one or more determinants acquired by the user-presented term acquisition means;
The dialogue apparatus according to any one of claims 1 to 7, further comprising knowledge vector updating means for updating so as to increase a value of an element of a knowledge vector with respect to one or more determination factors acquired by the apparatus presentation term acquisition means. The reception unit
Voice receiving means for receiving voice input by the user;
Voice recognition means for recognizing the voice and converting it into a character string;
The sentence output unit
The dialogue apparatus according to any one of claims 1 to 8, wherein the sentence composed by the sentence constructing unit is output as voice. On the storage medium,
Knowledge storing two or more pieces of spot information including a spot, one or more determinants that are factors for determining the spot, and an evaluation value indicating the evaluation of each of the one or more determinants of the spot Base and
2 or more having sentence pattern information that is information indicating a sentence output by the interactive device or a pattern of a sentence output by the interactive device, and evaluation information of sentence pattern information used when selecting the sentence pattern information Information recommendation methods,
User state information having a preference vector that is information indicating a user's state and indicating a user's preference for one or more determinants and a knowledge vector indicating a user's knowledge for each of the one or more determinants Storing and
A dialogue method realized by a reception unit, a score calculation unit, a sentence composition unit, a sentence output unit, and a user state information update unit,
The reception unit receives a sentence input by a user; and
The score calculation unit applies the user state information to evaluation information included in each of the two or more information recommendation methods stored in the storage medium, and calculates two or more scores for the two or more information recommendation methods. A score calculating step,
The sentence composing unit acquires sentence pattern information of one information recommendation method using two or more scores calculated in the score calculating step, and composes a sentence from the sentence pattern information;
The sentence output unit outputs a sentence composed in the sentence composition step;
The user status information update unit acquires at least one spot or one or more determinants from one or more sentences out of the sentences received in the receiving step or the sentences output in the sentence output step. And using the one or more spots or one or more determinants, the user state information updating step for updating the user state information of the storage medium,
The score calculating step includes:
The user status information updated by the user status information update unit is applied to evaluation information of each of the two or more information recommendation methods stored in the information recommendation method storage unit, and 2 for each of the two or more information recommendation methods. An interactive method for calculating the above score. On the storage medium,
Knowledge storing two or more pieces of spot information including a spot, one or more determinants that are factors for determining the spot, and an evaluation value indicating the evaluation of each of the one or more determinants of the spot Base and
2 or more having sentence pattern information that is information indicating a sentence output by the interactive device or a pattern of a sentence output by the interactive device, and evaluation information of sentence pattern information used when selecting the sentence pattern information Information recommendation methods,
User state information having a preference vector that is information indicating a user's state and indicating a user's preference for one or more determinants and a knowledge vector indicating a user's knowledge for each of the one or more determinants Storing and
Computer
A reception unit for receiving a sentence input by the user;
A score calculation unit that applies the user status information to evaluation information of each of the two or more information recommendation methods stored in the storage medium, and calculates two or more scores for the two or more information recommendation methods;
Using the two or more scores calculated by the score calculation unit, acquiring sentence pattern information possessed by one information recommendation method, and constructing a sentence from the sentence pattern information;
A sentence output unit for outputting a sentence formed by the sentence constructing unit;
At least one spot or one or more determinants are obtained from one or more sentences of the sentence received by the reception unit or the sentence output unit, and the one or more spots or one or more A program that causes a user status information update unit that updates user status information of the storage medium to function using a determination factor,
The score calculation unit
The user status information updated by the user status information update unit is applied to evaluation information of each of the two or more information recommendation methods stored in the information recommendation method storage unit, and 2 for each of the two or more information recommendation methods. A program that functions as the one that calculates the above score.

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