WO2023175910A1 - 意思決定支援システム、意思決定支援方法および記録媒体 - Google Patents

意思決定支援システム、意思決定支援方法および記録媒体 Download PDF

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Publication number
WO2023175910A1
WO2023175910A1 PCT/JP2022/012639 JP2022012639W WO2023175910A1 WO 2023175910 A1 WO2023175910 A1 WO 2023175910A1 JP 2022012639 W JP2022012639 W JP 2022012639W WO 2023175910 A1 WO2023175910 A1 WO 2023175910A1
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Prior art keywords
objective function
race
optimal solution
support system
decision
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English (en)
French (fr)
Japanese (ja)
Inventor
文秀 瀧本
宗裕 橋本
慎之介 西本
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NEC Corp
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NEC Corp
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Priority to JP2024507413A priority Critical patent/JP7816490B2/ja
Priority to PCT/JP2022/012639 priority patent/WO2023175910A1/ja
Publication of WO2023175910A1 publication Critical patent/WO2023175910A1/ja
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/34—Betting or bookmaking, e.g. Internet betting

Definitions

  • the present invention relates to a decision support system and the like.
  • the learning device of Patent Document 1 generates an objective function for determining an optimal solution by inverse reinforcement learning based on decision history data.
  • the program programming program in Patent Document 2 uses a learning model to organize athletes who will participate in a race.
  • the learning device of Patent Document 1 and the program programming program of Patent Document 2 may not be able to present optimal proposals in decision-making regarding race results of publicly managed competitions.
  • the purpose is to provide a decision-making support system, etc. that can easily make decisions regarding the race results of publicly managed competitions.
  • the decision support system of the present invention includes an acquisition means for acquiring information regarding races in publicly managed competitions, and a decision support system that is generated in advance by inverse reinforcement learning based on the decision history regarding prediction of race results in publicly managed competitions.
  • the decision-making support method of the present invention acquires information regarding races in publicly managed competitions, and uses objective functions generated in advance by inverse reinforcement learning based on decision history regarding prediction of race results in publicly managed competitions, and the acquired race results in publicly managed competitions.
  • the optimal solution for predicting the race result is determined using the information regarding the race result, and information regarding the prediction of the race result is output based on the determined optimal solution.
  • the recording medium of the present invention includes a process for acquiring information regarding races in publicly managed competitions, an objective function generated in advance by inverse reinforcement learning based on a decision history regarding prediction of race results in publicly managed competitions, and an acquired race result in publicly managed competitions.
  • a non-decision support program that causes a computer to execute the process of determining the optimal solution for predicting the race result using information about the race result, and the process of outputting information regarding the prediction of the race result based on the determined optimal solution. Record temporarily.
  • FIG. 1 is a diagram showing an example of a configuration of a first embodiment of the present invention.
  • FIG. 1 is a diagram showing an example of the configuration of a decision support system according to a first embodiment of the present invention. It is a figure showing an example of a display screen in a 1st embodiment of the present invention. It is a figure showing an example of a display screen in a 1st embodiment of the present invention. It is a figure showing an example of a display screen in a 1st embodiment of the present invention. It is a figure showing an example of a display screen in a 1st embodiment of the present invention. It is a figure showing an example of a display screen in a 1st embodiment of the present invention. It is a figure showing an example of a display screen in a 1st embodiment of the present invention. FIG.
  • FIG. 3 is a diagram showing an example of the operation flow of the decision support system according to the first embodiment of the present invention.
  • FIG. 3 is a diagram showing an example of the operation flow of the decision support system according to the first embodiment of the present invention. It is a figure showing an example of composition of a 2nd embodiment of the present invention. It is a figure showing an example of composition of a decision support system of a 2nd embodiment of the present invention. It is a figure showing an example of a display screen in a 2nd embodiment of the present invention. It is a figure showing an example of a display screen in a 2nd embodiment of the present invention. It is a figure showing an example of a display screen in a 2nd embodiment of the present invention. It is a figure showing an example of a display screen in a 2nd embodiment of the present invention.
  • FIG. 7 is a diagram schematically showing the correspondence between language space and intention space in the second embodiment of the present invention.
  • FIG. 7 is a diagram schematically showing the correspondence between language space and intention space in the second embodiment of the present invention.
  • FIG. 7 is a diagram schematically showing the correspondence between language space and intention space in the second embodiment of the present invention.
  • FIG. 7 is a diagram schematically showing the correspondence between language space and intention space in the second embodiment of the present invention.
  • FIG. 7 is a diagram schematically showing the correspondence between language space and intention space in the second embodiment of the present invention.
  • It is a figure showing an example of a display screen in a 2nd embodiment of the present invention.
  • It is a figure showing an example of a display screen in a 2nd embodiment of the present invention.
  • FIG. 1 is a diagram showing an example of an information processing system according to this embodiment.
  • the information processing system includes a decision support system 10, a user terminal device 20, and an information management server 30.
  • the decision support system 10 is connected to a user terminal device 20 via a network. Further, the decision support system 10 is connected to an information management server 30 via a network.
  • the decision support system 10 is a system that supports decision making regarding the race results of publicly managed competitions.
  • An example of a publicly managed game is horse racing.
  • Publicly managed competitions may be bicycle races, boat races, or auto races. Examples of publicly managed competitions are not limited to those mentioned above, and the type of competition does not matter as long as it is a competition held by a public institution for gambling purposes.
  • Decision-making regarding race results in publicly managed competitions is, for example, determining the combination of voting tickets when purchasing voting tickets for races in publicly managed competitions.
  • the decision regarding the race result of the publicly managed competition may be a decision on predicting the finish order of the race or a decision on the race for which voting tickets are to be purchased.
  • the combination of voting tickets is determined by determining, for example, when purchasing voting tickets, what kind of horse numbers are used for two consecutive wins (horse racing) and frame numbers are two consecutive winning combinations (fraction). The key is to decide whether to buy a combination.
  • the combination of voting tickets is not limited to the above example.
  • examples of decision-making regarding race results in publicly managed competitions are not limited to determining the combination of voting tickets.
  • the decision support system 10 uses, for example, a pre-generated objective function to determine the optimal solution for predicting race results.
  • the objective function is generated by inverse reinforcement learning based on the history of decision-making regarding prediction of race results in public competitions.
  • the optimal solution in predicting race results is also called an optimization result.
  • the decision support system 10 then outputs information regarding the race results to the user terminal device 20 based on the determined optimal solution.
  • the objective function is a function used to determine the optimal solution for predicting race results.
  • the feature amounts correspond to explanatory variables in the objective function.
  • the feature amount is, for example, information regarding a race that can influence decision making.
  • the feature amounts are, for example, pedigree and race distance.
  • the weighting coefficient of the feature amount corresponds to the coefficient of the explanatory variable in the objective function.
  • the weighting coefficient of the feature amount is an index indicating how much importance is given to information regarding the race in decision making.
  • the combination of the feature quantities included in the objective function generated based on the decision-making history regarding prediction of race results in public competitions and the weighting coefficients of the feature quantities is also called intention.
  • the combination of the feature amounts included in the objective function and the weighting coefficients of the feature amounts reflects the decision-making intention of the person whose decision history is used when generating the objective function.
  • the intention is explained using natural language (including words and sentences) according to decision-making tendencies, for example.
  • a feature amount having a weighting coefficient greater than or equal to a predetermined value in the objective function may be used as a word corresponding to a decision-making tendency.
  • leg quality is set as a word according to the tendency of decision making.
  • the objective function is generated, for example, reflecting the intention of an expert.
  • a plurality of objective functions may be generated, for example, reflecting the intentions of each expert.
  • the decision support system 10 may determine the optimal solution for predicting the race result using any one of the plurality of objective functions generated for each intention.
  • the decision-making history regarding the prediction of the race result of a publicly managed competition includes status data regarding the publicly managed competition and behavioral data regarding the publicly managed competition.
  • the status data regarding publicly managed competitions is, for example, information regarding races in publicly managed competitions.
  • Information regarding races in public competitions includes, for example, race conditions, odds, and attributes of athletes participating in the races.
  • the competition body is the main body that participates in the race.
  • the attributes of the competition objects are information about each competition object participating in the race.
  • the publicly managed competition is horse racing
  • the competition object is a horse.
  • the attributes of the sport also include information regarding the jockey.
  • the publicly managed competition is a bicycle race, a boat race, or an auto race
  • the competing objects are athletes.
  • the attributes of the competition object may include information about a bicycle, a boat, or a motorcycle. Information regarding races is not limited to the above example.
  • Behavioral data related to publicly managed competitions is, for example, the result of a decision made intentionally by a person regarding a race in a publicly managed competition.
  • a decision made with intention is, for example, a decision made by a person making the decision based on a policy that the person has.
  • the decision history includes information about a certain race in the past and the decision made by an expert regarding the combination of voting tickets for that race. It's a decision.
  • the decision regarding the combination of voting tickets is, for example, the determination of the purchasing pattern of voting tickets.
  • the voting ticket purchase pattern is, for example, a combination of voting tickets to be purchased.
  • the voting ticket purchase pattern may be a combination of voting tickets and a purchase amount for each voting ticket.
  • the information regarding a certain race in the past is status data.
  • the combination of voting tickets determined by the expert for the race is behavioral data.
  • the decision-making history may be data on decisions made by experts or commentators.
  • the decision-making support system 10 generates an objective function by inverse reinforcement learning, for example, based on the decision-making history regarding prediction of race results in publicly managed competitions in past races.
  • the generated objective function is used to determine the optimal solution in predicting race results.
  • the decision support system 10 generates an objective function by determining weighting coefficients of features included in the objective function, for example, by inverse reinforcement learning using race information and decision history in past races. .
  • the weighting coefficient of the feature included in the objective function is the coefficient of the explanatory variable corresponding to the feature in the objective function.
  • the decision support system 10 may use an objective function generated outside the decision support system 10 to determine the optimal solution for predicting the race result.
  • the user terminal device 20 is, for example, a terminal device owned by a person who uses the optimal solution in predicting the race result determined by the decision support system 10.
  • a person who uses the optimal solution in predicting a race result is, for example, a person who purchases a voting ticket for a race.
  • the person who utilizes the optimal solution in predicting the race result may be the person in charge of devising the organization of the race. Further, the person who uses the optimal solution in predicting the race result may be a reporter or a commentator.
  • the person who uses the optimal solution in predicting the race result is not limited to the above example.
  • the information management server 30 is, for example, a server that holds information regarding races in publicly managed competitions.
  • the information regarding the race is, for example, the conditions of the race and the attributes of the athletes participating in the race.
  • the competition body is the main body that participates in the race.
  • the attributes of the competition objects are information about each competition object participating in the race.
  • the publicly managed competition is horse racing
  • the competition object is a horse.
  • the attributes of the sport also include information regarding the jockey.
  • the publicly managed competition is a bicycle race, a boat race, or an auto race
  • the competing objects are athletes.
  • the attributes of the competition object may include information about a bicycle, a boat, or a motorcycle. Information regarding races is not limited to the above example.
  • the decision support system 10 acquires information regarding races in public competitions from the information management server 30. Then, the decision support system 10 determines the optimal solution for predicting the race result using the information regarding the race of the publicly managed competition acquired from the information management server 30 and the objective function. The decision support system 10 then outputs the optimal solution for predicting the race result to the user terminal device 20.
  • the decision support system 10 may acquire information regarding races in public competitions from a plurality of information management servers 30. Further, the decision support system 10 may acquire information regarding races in public competitions from the user terminal device 20.
  • the decision support system 10 may output the optimal solution for predicting the race result to the plurality of user terminal devices 20.
  • the decision support system 10 may output the optimal solution for predicting the race result to the user terminal devices 20 used by a plurality of users.
  • the number of user terminal devices 20 and information management servers 30 may be set as appropriate.
  • FIG. 2 is a diagram showing an example of the configuration of the decision support system 10.
  • the decision support system 10 includes an acquisition section 11 , a determination section 12 , an output section 13 , a generation section 14 , and a storage section 15 .
  • the acquisition unit 11 acquires information regarding races in publicly managed competitions.
  • Information regarding races in public competitions is, for example, information that can be related to determining the optimal solution in predicting race results.
  • the acquisition unit 11 acquires, for example, race conditions, odds, and attributes of competition objects as information regarding races in publicly managed competitions.
  • the acquisition unit 11 acquires odds for each type of voting ticket.
  • the race conditions include, for example, information regarding the stadium. Further, the race conditions may include race setting conditions and conditions that the runners must satisfy.
  • the race conditions include, for example, at least one of distance, race track type, race horse conditions, weight, race rating, race track, weather, race track condition, number of race horses, and running direction.
  • the type of horse track is, for example, grass, dirt, or obstacles.
  • the conditions for a horse to run are defined by, for example, the age and sex of the horse.
  • the condition of the horse track is, for example, information indicating the amount of water contained in the horse track.
  • the running direction is, for example, information indicating whether the race is run counterclockwise or clockwise. Race conditions when the publicly managed competition is horse racing are not limited to the above example.
  • the attributes of the competition are the attributes of the horses running.
  • the attributes of the runners are information about each runner.
  • the attributes of the running horses include, for example, lane number, slot number, odds, age, gender, weight, weight change, blood data, muscle mass, training status, health condition, rest history, race participation history, weight load, leg quality, At least one of race record, sire horse, dam horse, owner, stable, trainer, and producer.
  • the lane number may be a horse number.
  • the training situation is, for example, time and time change for each distance during training.
  • the foot quality is set, for example, by classification of a runaway horse, a leading horse, a lead horse, or a chasing horse.
  • the attributes of the running horses may include the race records of the sire horse and dam horse.
  • race results include, for example, race conditions in past races, attributes of horses running in races, prize money earned, and race developments.
  • Lace development includes, for example, positioning and difference in placement. The positioning is, for example, the ranking and time in each section when the entire section of the race is divided into predetermined distances.
  • the difference in finish is, for example, the time difference between a horse that is higher in the ranking or a horse that is lower in the ranking.
  • the attributes of the running horses are not limited to the above example.
  • the race conditions include, for example, at least one of the race track, race distance, and weather.
  • the attributes of the competition body include at least one of the athlete's height, weight, age, leg quality, odds, and match record.
  • examples of race conditions and attributes of the race bodies are not limited to the above.
  • the race conditions include, for example, at least one of the race track, race distance, and weather.
  • the attributes of the competition body include at least one of the athlete's height, weight, age, rank, odds, and match record.
  • examples of race conditions and attributes of competition objects are not limited to the above.
  • the race conditions include, for example, at least one of the race track, the presence or absence of a handicap, and the weather.
  • the attributes of the athlete are at least one of the athlete's height, weight, age, affiliation, class, odds, and match record.
  • the attributes of the competition object may include start display information. Examples of race conditions and attributes of race objects when the publicly managed competition is an auto race are not limited to the above.
  • the acquisition unit 11 may acquire from the user terminal device 20 the selection result of the objective function used to determine the optimal solution.
  • the selection result of the objective function may be acquired from the user terminal device 20, for example, by inputting the selection of the objective function to the user terminal device 20 through the operation of a person who uses the optimal solution in predicting the race result. Further, the acquisition unit 11 may acquire from the user terminal device 20 a selection of display items that is input to the user terminal device 20 by an operation of a person who uses the optimal solution in predicting the race result.
  • the acquisition unit 11 acquires the decision history regarding races in publicly managed competitions as data for generating the objective function. You may.
  • the acquisition unit 11 stores, for example, the acquired decision-making history regarding races in public competitions in the storage unit 15.
  • the determining unit 12 determines the optimal solution for predicting race results using the objective function and information regarding races in publicly managed competitions.
  • the objective function is generated in advance using inverse reinforcement learning based on the decision-making history related to predicting race results in publicly managed competitions.
  • the determining unit 12 determines an optimal solution for predicting race results by solving an optimization problem using, for example, an objective function and information regarding races in publicly managed competitions.
  • the determining unit 12 may extract the reason for determining the optimal solution in predicting the race result. For example, the determining unit 12 extracts a feature quantity that has a larger influence on determining the optimal solution than other items as the reason for determining the optimal solution. A feature that has a greater influence on determining the optimal solution than other items is, for example, a feature that has a larger weighting coefficient than the other features. Further, the determining unit 12 outputs a feature amount whose degree of influence on determining the optimal solution is equal to or higher than a preset standard as a reason for determining the optimal solution.
  • the determining unit 12 may output information regarding intentions that have a greater influence on determining the optimal solution than other intentions as the reason for determining the optimal solution.
  • the information regarding the intention is, for example, a mathematical formula corresponding to the intention or a word indicating the intention.
  • the determining unit 12 outputs information regarding intentions whose influence on determining the optimal solution is greater than a preset standard as the reason for determining the optimal solution. Good too.
  • the determining unit 12 may determine the optimal solution for predicting the race result using any one of the plurality of objective functions.
  • the determining unit 12 determines the optimization result using, for example, an objective function according to the attribute of the person who uses the optimal solution in predicting the race result.
  • the attribute of the person who uses the optimal solution in predicting the race result is, for example, the position of the person who uses the optimal solution in predicting the race result.
  • the attributes of the person who uses the optimal solution in predicting the race result are set, for example, by classification of voting ticket purchaser, reporter, commentator, and race organizer.
  • the attributes of the person who uses the optimal solution in predicting the race result may be set for beginners, intermediates, and experts.
  • the attributes of the person who uses the optimal solution are not limited to the above.
  • the determining unit 12 may determine the optimal solution for predicting the race result using an objective function generated for each person whose decision-making history is used.
  • the determining unit 12 uses, for example, either model A that uses an objective function generated based on commentator A's decision-making history, or model B that uses an objective function generated based on commentator B's decision-making history, An optimal solution for predicting race results may be determined.
  • the determining unit 12 uses, for example, an objective function to determine a combination of voting tickets that will result in a higher payout amount. For example, the determining unit 12 determines a combination of voting tickets such that the refund amount is higher than the purchase amount. For example, the determining unit 12 determines a combination of voting tickets such that the refund amount is higher than the purchase amount, using the budget for purchasing voting tickets as a constraint condition.
  • the determining unit 12 may determine a combination of voting tickets for a plurality of races as the optimal solution. In order to obtain a high return, the determining unit 12 may determine a combination of voting tickets from among voting tickets with odds equal to or higher than a reference value as an optimal solution. Furthermore, in order to prevent speculative purchases, the determining unit 12 may determine a combination of voting tickets from among voting tickets whose odds are less than a standard as an optimal solution.
  • the determining unit 12 may determine the race for which a voting ticket is to be purchased as the optimal solution.
  • the determination unit 12 may determine a race for which a voting ticket is to be purchased from among races that satisfy the constraint conditions.
  • the determining unit 12 determines, for example, a race for which a voting ticket is to be purchased from among races within a certain period of time after the person using the optimal solution earns income.
  • the determining unit 12 may determine the order of race finish as the optimal solution.
  • the prediction of the race result determined as the optimal solution is not limited to the above example.
  • the output unit 13 outputs information regarding prediction of the race result based on the optimal solution determined by the determination unit 12.
  • the information regarding the prediction of the race result is, for example, at least one of the optimal solution determined by the determining unit 12, the reason for determining the optimal solution, and information generated using the determined optimal solution.
  • Information regarding prediction of race results is not limited to the above.
  • the optimal solution is a combination of voting tickets to be purchased
  • the information generated using the determined optimal solution is, for example, the payout amount.
  • the output unit 13 outputs the determined optimal solution to the user terminal device 20, for example.
  • the output unit 13 may output the reason for determining the optimal solution together with the determined optimal solution.
  • the output unit 13 may output the determined optimal solution to a display device (not shown) connected to the decision support system 10. Further, the output unit 13 may output the determined optimal solution to a server that distributes the optimal solution in predicting the race result.
  • the output unit 13 may output the objective function or the weighting coefficient of the feature amount included in the objective function.
  • the weighting coefficient of the feature included in the objective function is the coefficient of the explanatory variable to which the feature corresponds. Further, the output unit 13 may visualize and output the weighting coefficients of the feature amounts included in the objective function. The output unit 13 visualizes and outputs the weighting coefficients included in the objective function by displaying them on a graph, for example.
  • the output unit 13 When the optimal solution in predicting the race result is a combination of voting tickets to be purchased, the output unit 13 outputs the combination of voting tickets indicated as the optimal solution as well as the expected income and expenditure when purchasing voting tickets with the combination. It's okay.
  • the output unit 13 may output the optimal solution for predicting the race result, as well as images of the runners included in the optimal solution and information regarding the runners.
  • the output unit 13 may output either one of the optimal solution in predicting the race result, an image of the runner included in the optimal solution, and information regarding the runner.
  • FIG. 3 shows an example of the display screen of the optimal solution in predicting the race result.
  • the combination of voting tickets to be purchased when purchasing voting tickets for horse racing is displayed as the optimal solution.
  • the optimal solution for the combination of voting tickets to be purchased for the ninth race to be held at the Tokyo Racecourse is displayed.
  • the optimal combination of voting tickets to purchase is displayed when the budget is 1000 yen.
  • the optimal solution for the combination of voting tickets is displayed using the type of voting ticket, horse number or slot number, and purchase amount.
  • odds for each voting ticket are displayed.
  • the combinations of 600 yen for horse group 3-5, 200 yen for horse group 3-9, and 200 yen for frame group 2-3 are displayed as the optimal combinations of voting tickets.
  • FIG. 4 shows an example of a display screen that further displays the refund amount and income and expenditure in the example of the display screen of FIG. 3.
  • the refund amount in the event that the predicted finish is correct is displayed as the refund amount.
  • the bottom row displays the income and expenditure for each horse placed in the race.
  • the balance display in the example of the display screen in FIG. 4 shows that if the first place is horse number 3, and the second place is horse number 5, the balance will be an additional 720 yen.
  • the balance display in the example of the display screen in Figure 4 shows that if 1st place is run by horse number 3 and 2nd place is run by horse number 9, the balance will be an additional 320 yen. .
  • FIG. 5 shows an example of a display screen in which the reason for determining the optimal solution is further displayed in the example of the display screen of FIG. 3.
  • the reason for determining the optimal solution is displayed in the reason column.
  • a feature amount that has a greater influence on determining the optimal solution than other feature amounts is displayed.
  • a feature quantity that has a larger influence on determining the optimal solution than other feature quantities is, for example, a feature quantity whose weighting coefficient is larger than that of the other feature quantities.
  • pedigree and race distance are displayed as reasons for the decision.
  • the example of the display screen in FIG. 5 shows, for example, that the reason why horse race 3-5 is the optimal solution is the pedigree of the horses entering the race.
  • FIG. 6 shows an example of a display screen that displays information on the horses included in the optimal solution.
  • an image of a horse running in lane 3 and information related to the horse running are displayed as reference information.
  • the output unit 13 may add information on a plurality of runners to the optimization result and output the result. Further, the output unit 13 may output information corresponding to a runner clicked or tapped on the display screen, for example.
  • FIG. 7 shows an example of a display screen that displays the objective function used to determine the optimal solution.
  • the expression of the objective function used to determine the optimal solution or the weighting coefficient of the feature quantity included in the objective function is displayed in the prediction expression column at the bottom.
  • the generation unit 14 When the objective function is generated in the decision-making support system 10, the generation unit 14 generates it by inverse reinforcement learning based on the decision-making history regarding prediction of race results in public competitions.
  • the objective function determines the optimal solution for predicting race results from information regarding races in public competitions.
  • the generation unit 14 generates the objective function by learning, for example, a history of decision making regarding prediction of race results in publicly managed competitions by inverse reinforcement learning.
  • the generation unit 14 may generate the objective function as a decision-making model including a combination of feature quantities included as explanatory variables in the objective function, weighting coefficients for each feature quantity, and constraint conditions.
  • the generation unit 14 learns the behavioral data included in the decision-making history regarding prediction of race results in publicly managed competitions as the optimal solution, and generates an optimization index, that is, an objective function.
  • the generation unit 14 generates an objective function by generating a plurality of linear forms and learning rules for selecting the linear forms.
  • the generation unit 14 generates an objective function that reflects the intention corresponding to the decision history by repeatedly performing optimization using a reward and updating the weighting coefficient of the objective function using the decision history.
  • the generation unit 14 When generating the objective function, the generation unit 14 sets the weighting coefficients of the feature amounts included in the objective function using temporary values. The generation unit 14 uses the objective function when performing inverse reinforcement learning using the decision history. The tentative values of the weighting coefficients are set randomly, for example. Then, the generation unit 14 generates an objective function using the set weighting coefficients. After generating the objective function, the generation unit 14 solves the optimization problem using the state data of the decision history and the objective function, and determines an optimal solution. After determining the optimal solution, the generation unit 14 compares the determined optimal solution with behavioral data of the decision-making history.
  • the generation unit 14 updates the weighting coefficients based on the comparison results so that the difference between the optimal solution determined using a predetermined algorithm and the behavioral data of the decision history becomes small, and generates an objective function.
  • the generation unit 14 solves the optimization problem using the state data of the decision history, the generated objective function, and the determined optimal solution, and repeats the process of determining the optimal solution.
  • the generation unit 14 ends the learning when the difference between the determined optimal solution and the behavioral data of the decision history satisfies a criterion set in advance as an end condition. Then, the generation unit 14 stores the generated objective function in the storage unit 15, for example.
  • the generation unit 14 generates the objective function by, for example, maximum entropy inverse reinforcement learning.
  • the generation unit 14 may generate the objective function by sparse inverse reinforcement learning.
  • the generation unit 14 When generating an objective function by sparse inverse reinforcement learning, the generation unit 14 generates the objective function by, for example, assigning weights to all candidates for feature quantities in advance and selecting feature quantities according to the learning results. generate. Further, the generation unit 14 may allocate a plurality of objective functions so that they can be selected according to attributes.
  • a method for generating an objective function is described in, for example, International Publication No. 2021/130915.
  • the storage unit 15 stores, for example, an objective function that the determination unit 12 uses to determine the optimal solution.
  • the storage unit 15 stores the plurality of objective functions.
  • the storage unit 15 may store the updated weighting coefficient of the feature quantity.
  • the storage unit 15 may store decision history for past race predictions, which is used to generate the objective function.
  • Each of the above data may be stored in a storage means other than the storage unit 15.
  • the user terminal device 20 acquires the optimal solution for predicting the race result from the decision support system 10. Then, the user terminal device 20 outputs the optimal solution for predicting the race result to, for example, a display device (not shown).
  • the user terminal device 20 When the user selects an objective function to determine the optimal solution, the user terminal device 20 obtains an input for selecting the objective function input by the user's operation, for example. The user terminal device 20 may acquire the name of the objective function input by the user's operation. The user terminal device 20 then outputs the input result regarding the selection of the input objective function to the decision support system 10.
  • a smartphone, a tablet computer, a notebook computer, or a desktop computer is used as the user terminal device 20.
  • the terminal device used for the user terminal device 20 is not limited to the above example.
  • the information management server 30 is, for example, a server that stores or manages information regarding races in publicly managed competitions.
  • the information management server 30 may be a plurality of servers installed according to the content of information regarding races in public competitions. Information regarding races in public competitions may be stored in a storage device managed by the information management server 30.
  • FIG. 8 is a diagram showing an example of an operation flow when the decision support system 10 determines the optimal solution in predicting the race result.
  • the acquisition unit 11 acquires information regarding races in publicly managed competitions (step S11).
  • the acquisition unit 11 acquires information regarding races in publicly managed competitions, for example, from the information management server 30.
  • the determining unit 12 uses the objective function generated in advance and the acquired information regarding the race in the publicly managed competition to determine the optimal solution for predicting the race result (step S12).
  • the objective function is generated using inverse reinforcement learning based on the decision-making history related to predicting race results in public competitions.
  • the determining unit 12 determines the optimal solution for predicting the race result by solving an optimization problem using, for example, the objective function and the acquired information regarding the publicly managed race.
  • the output unit 13 outputs the optimal solution determined by the determining unit 12 (step S13).
  • the output unit 13 outputs the optimal solution for predicting the race result to the user terminal device 20, for example.
  • the user terminal device 20 After acquiring the optimal solution for predicting the race result, the user terminal device 20 outputs the optimal solution for predicting the race result, for example, to a display device (not shown).
  • FIG. 9 is a diagram illustrating an example of an operation flow when the decision support system 10 generates an objective function.
  • the decision support system 10 uses the generated objective function when determining the optimal solution for predicting race results in public competitions.
  • the acquisition unit 11 acquires a history of decision-making regarding prediction of race results in publicly managed competitions in races held in the past (step S21).
  • the generation unit 14 sets weighting coefficients of the feature amounts included in the objective function using temporary values.
  • the generation unit 14 uses the objective function when performing inverse reinforcement learning using the decision history. After setting the weighting coefficient using the temporary value, the generation unit 14 generates an objective function using the set weighting coefficient (step S22).
  • the generation unit 14 After generating the objective function, the generation unit 14 solves the optimization problem using the decision history and the objective function, and determines the optimal solution (step S23). After determining the optimal solution, the generation unit 14 compares the determined optimal solution with behavioral data of the decision-making history (step S24).
  • the generation unit 14 stores the generated objective function (step S26).
  • the generation unit 14 stores the generated objective function in the storage unit 15, for example.
  • the stored objective function is used by the determining unit 12 to determine the optimal solution.
  • the generation unit 14 updates the weighting coefficient of the feature amount using a preset algorithm (step S27). After updating the weighting coefficients of the feature amounts, the generation unit 14 generates an objective function using the updated weighting coefficients (step S28). After generating the objective function, the generation unit 14 repeats the processing from step S23.
  • the decision-making support system 10 of this embodiment uses an objective function generated in advance by inverse reinforcement learning based on the decision-making history regarding prediction of race results in publicly managed competitions, and information regarding races in publicly managed competitions. Determine the optimal solution in prediction. Therefore, by using the decision-making support system 10, it becomes possible to easily obtain an optimal solution for predicting the race result based on the decision-making history. Therefore, by using the decision support system 10, it is possible to easily make decisions regarding the race results of publicly managed competitions.
  • the decision support system 10 can perform optimization by, for example, outputting information on the income and expenditure for each combination of voting tickets based on the determination of the optimal solution. This makes it easier for the user to decide whether to apply the result of the decision.
  • the decision support system 10 uses the optimal solution in predicting the race result by using an objective function depending on the person who uses the optimal solution in predicting the race result, for example. It is possible to output the optimal solution suitable for the person who uses the software.
  • FIG. 10 is a diagram showing an example of the information processing system of this embodiment.
  • the information processing system includes a decision support system 40, a user terminal device 20, and an information management server 30.
  • the decision support system 40 is connected to the user terminal device 20 via a network. Further, the decision support system 40 is connected to the information management server 30 via a network.
  • the numbers of user terminal devices 20 and information management servers 30 are set as appropriate.
  • the functions of the user terminal device 20 and the information management server 30 of this embodiment are the same as those of the user terminal device 20 and the information management server 30 of the first embodiment.
  • the decision-making support system 10 of the first embodiment uses an objective function generated in advance by inverse reinforcement learning based on the decision-making history regarding prediction of race results in publicly managed competitions, and acquired information regarding races in publicly managed competitions. , determine the optimal solution in predicting race results. The decision support system 10 then outputs the optimal solution for predicting the race result of the publicly managed competition.
  • the decision support system 40 of the present embodiment can, for example, change the weighting coefficient of the feature amount included in the objective function used to determine the optimal solution in predicting the race result.
  • the decision support system 40 visualizes and outputs embedded expressions of words that correspond to combinations of weighting coefficients of feature quantities included in the objective function.
  • the words indicate, for example, trends in decision-making regarding prediction of race results in public competitions.
  • a word corresponds to an intention, for example.
  • the decision support system 40 searches for a sentence that includes an embedded expression of a word that corresponds to a combination of weighting coefficients of feature quantities included in the objective function, for example.
  • the decision support system 40 updates the objective function using, for example, a combination of weighting coefficients of features included in the objective function corresponding to the embedded representation of the word.
  • FIG. 11 is a diagram showing an example of the configuration of the decision support system 40.
  • the decision support system 40 includes an acquisition unit 11, a determination unit 12, an output unit 41, a generation unit 14, a storage unit 15, a reception unit 42, an update unit 43, a coefficient prediction unit 44, and an expression prediction unit. 45, a query generation section 46, and a search section 47.
  • the configuration and functions of the acquisition unit 11, determination unit 12, generation unit 14, and storage unit 15 of the decision support system 40 are the same as the acquisition unit 11, determination unit 12, and output unit 13 of the decision support system 10 of the first embodiment. , the generation unit 14 and the storage unit 15, respectively.
  • the output unit 41 outputs, for example, a screen related to changing the weighting coefficient of the objective function. Further, the output unit 41 outputs, for example, a search result obtained by performing a search using a search query.
  • the output unit 41 outputs, for example, a display screen having an input field for the weighting coefficient of the objective function as a screen related to changing the weighting coefficient of the objective function.
  • the output unit 41 may display the weighting coefficients of the objective function as a graph and output a display screen for changing the weighting coefficients on the graph.
  • the output unit 41 When outputting the weighting coefficients of the objective function as a graph, the output unit 41 outputs, for example, a graph centered on feature quantities that make a large contribution to determining the optimal solution by the objective function among the feature quantities.
  • the output unit 41 outputs, for example, a two-dimensional graph centered on two feature quantities that make a large contribution to determining the optimal solution by the objective function among the feature quantities. Further, the output unit 41 may output, for example, a graph centered on feature quantities that have contrasting effects on the determination of the optimal solution by the objective function among the feature quantities.
  • the output unit 41 may output a three-dimensional or more-dimensional graph centered on three or more feature quantities that greatly contribute to determining the optimal solution by the objective function among the feature quantities. Further, the output unit 41 may display the result of converting the multidimensional feature amount into two dimensions through dimension reduction processing as a graph.
  • the output unit 41 outputs, for example, the result of searching for a text using a search query.
  • the text to be searched is, for example, a text related to predicting race results in publicly managed competitions.
  • the sentence to be searched is, for example, a sentence such as ⁇ I started racing in Tokyo 9 races and won big.'' ”
  • the output unit 41 outputs, to the user terminal device 20, for example, a sentence including an expression corresponding to the search query searched by the search unit 47.
  • the output unit 41 outputs posts that include expressions corresponding to the search query searched by the search unit 47.
  • the receiving unit 42 receives, for example, a change value for changing the weighting coefficient of the feature quantity included in the objective function.
  • the receiving unit 42 receives, for example, a change value for changing the weighting coefficient of the feature included in the objective function from the user terminal device 20.
  • a change value for changing the weighting coefficient of the feature quantity included in the objective function is input into the user terminal device 20 by the user of the user terminal device 20, for example.
  • the reception unit 42 may receive the change value from an input device (not shown) connected to the decision support system 40.
  • the reception unit 42 may accept changed values input by changing the weighting coefficients of the feature quantities displayed on the graph. .
  • the receiving unit 42 may receive a change value input by changing the display position of the weighting coefficient of the feature amount on the graph.
  • the reception unit 42 may receive a change value input by changing the display order in a list indicating weighting coefficients of feature amounts.
  • the receiving unit 42 When updating the objective function using a combination of weighting coefficients of feature quantities included in the objective function corresponding to the embedded representation of a word, the receiving unit 42 accepts the combination of weighting coefficients of feature quantities selected by the user. .
  • the reception unit 42 may receive an intention corresponding to a combination of weighting coefficients of feature amounts selected by the user.
  • the reception unit 42 receives the intention by acquiring, from the user terminal device 20, the selection result of the intention, which is input into the user terminal device 20 by the user's operation, for example.
  • the updating unit 43 updates the weighting coefficient of the feature quantity included in the objective function using the obtained value.
  • the updating unit 43 stores, for example, the objective function with updated weighting coefficients in the storage unit 15.
  • the updating unit 43 When updating the objective function based on the accepted intentions, the updating unit 43 extracts, for example, an objective function corresponding to each intention accepted by the accepting unit 42. Then, the updating unit 43 calculates the average value of the weighting coefficients for each feature quantity from the weighting coefficients of the feature quantities included in the extracted objective function. The updating unit 43 updates the objective function using the average value of each of the calculated weighting coefficients as a weighting coefficient of each feature amount. When calculating a new weighting coefficient from the weighting coefficients of the feature amounts included in the plurality of objective functions, the updating unit 43 may calculate the weighting coefficient using a value other than the average value.
  • the updating unit 43 may calculate a new weighting coefficient so that the influence of the weighting coefficient of the feature amount included in the objective function corresponding to the intention to be emphasized becomes greater.
  • the updated objective function is used by the determining unit 12 to determine the optimal solution for predicting the race result.
  • the updating unit 43 may update the objective function using a combination of weighting coefficients of features corresponding to the embedded expression of the word indicating the intention.
  • the updating unit 43 updates the objective function using, for example, a combination of weighting coefficients of feature amounts corresponding to the embedded expression of the word indicating the intention, which is predicted by the coefficient predicting unit 44.
  • FIG. 12 shows an example of a display screen that displays the objective function equation and accepts input for changing the weighting coefficient on the display screen of the optimal solution in predicting the race result in FIG. 3.
  • the objective function equation is displayed in the prediction equation column.
  • the weighting coefficient of the objective function is changed by, for example, rewriting the expression of the weighting coefficient of the objective function by a user's operation and pressing an update button.
  • FIG. 13 shows an example of a display screen that displays weighting coefficients of feature quantities included in the objective function on a two-dimensional graph.
  • the example display screen in FIG. 13 shows the weighting coefficients of x n and x m among the feature amounts included in the objective function as a graph.
  • "Current intention” in the example display screen of FIG. 13 indicates the weighting coefficient of the feature quantity included in the objective function used to determine the optimal solution.
  • “Intention of Mr. A's model” in the example of the display screen in FIG. 13 indicates the weighting coefficient of the feature amount included in the objective function generated using Mr. A's decision-making history.
  • “intention of Mr. B model” in the example of the display screen in FIG. 13 indicates the weighting coefficient of the feature quantity included in the objective function generated using Mr. B's decision-making history.
  • the weighting coefficient may be changed to correspond to the destination position.
  • FIG. 14 shows an example of a display screen that displays feature quantities that make a large contribution to determining the optimal solution in predicting race results as a list in order of magnitude of influence.
  • the value of the weighting coefficient is changed by, for example, changing the arrangement order of the feature amounts by operating a mouse and pressing an update button.
  • the updating unit 43 changes the weighting coefficients of the two feature quantities by exchanging the values of the weighting coefficients of the two feature quantities.
  • FIG. 15 is an example of a display screen that shows weighting coefficients of feature amounts corresponding to two intentions as a graph.
  • weighting coefficients of features included in different objective functions are shown at the upper end and at the latter stage.
  • the weighting coefficient of the feature can be changed by dragging the height of the feature graph. Changed to the value corresponding to the height.
  • the updating unit 43 changes the weighting coefficient of the feature amount using, for example, a value that corresponds to the height of the changed graph.
  • the coefficient prediction unit 44 predicts, for example, a combination of weighting coefficients of feature amounts corresponding to the embedded representation of a word.
  • the coefficient prediction unit 44 predicts a combination of weighting coefficients corresponding to an embedded expression based on a coefficient prediction model that has learned the relationship between an embedded expression of a word and a combination of weighting coefficients of a feature amount.
  • the coefficient prediction model is, for example, a trained model that has learned the relationship between the embedded representation of a word converted into a vector using a natural language processing model and the combination of weighting coefficients of feature amounts.
  • the natural language processing model for example, BERT (Bidirectional Encoder Representations from Transformers) can be used.
  • the expression prediction unit 45 predicts an expression corresponding to the weighting coefficient of the feature amount included in the objective function.
  • the expression prediction unit 45 predicts, for example, an embedded expression of a word corresponding to a weighting coefficient of a feature included in the objective function.
  • the expression prediction unit 45 predicts the embedded expression corresponding to the combination of weighting coefficients based on the expression prediction model that has learned the combination of weighting coefficients and the embedded expression of the word.
  • the embedded representation corresponding to the combination of weighting coefficients includes representations similar to the embedded representation corresponding to the combination of weighting coefficients.
  • FIG. 16 is a diagram schematically showing the correspondence between intention space and language space.
  • the intention space is a space represented by the feature amounts included in the objective function.
  • Linguistic space is a space represented by embedded representations of words.
  • a linguistic space is a space in which the semantic similarity of languages is defined as a continuous value.
  • the vertical and horizontal axes of the graph of the intention space are set using, for example, feature quantities selected from the weighting coefficients of the feature quantities included in the objective function.
  • the vertical and horizontal axes of the language space graph are set based on linguistic meaning. Expressions that are close to each other in the graph of language space are expressions that have similar meanings.
  • intention A in the intention space corresponds to "gamer” in the language space.
  • intention B in the intention space corresponds to "contrarian” in the language space.
  • the expression prediction unit 45 uses a prediction model generated by machine learning using these combinations as training data to predict the embedded expression of the word corresponding to the weighting coefficient of the feature amount.
  • FIG. 17 is a diagram schematically showing the correspondence between the intention space and the language space during prediction using the expression prediction model.
  • intention C in the intention space corresponds to "yamakke" in the language space.
  • the expression prediction model used by the expression prediction unit 45 predicts "Yamake" in the language space for the combination of feature amounts of intention C.
  • FIG. 18 is a diagram schematically showing the correspondence between the intention space and the language space during prediction using the expression prediction model, including the range of similarity in the language space.
  • intention C in the intention space corresponds to "Yamakke” in the language space.
  • Game Master is included in the similar range of "Yamakke”.
  • the expression prediction model may predict "Yamakke” and "Gamer” in the linguistic space for the combination of feature amounts of intention C.
  • FIG. 19 is a diagram schematically showing the correspondence between the language space and the intention space when calculating corresponding weighting coefficients from a plurality of embedded expressions in the language space.
  • the weighting coefficient of the feature amount is calculated using the formula: (c ⁇ (c+b)) ⁇ (weighting factor of intention C)+(b ⁇ (c+b)) ⁇ (weighting factor of intention B).
  • the query generation unit 46 generates a search query based on the embedded expression predicted by the expression prediction unit 45, for example. For example, the query generation unit 46 generates a search query for searching for an embedded expression of a word.
  • the embedded expression of a word corresponds to a combination of weighting coefficients predicted by the expression prediction unit 45 using an expression prediction model.
  • the query generation unit 46 generates, as a search query, a program using SQL to search for an embedded expression of a word corresponding to a combination of weighting coefficients of feature amounts.
  • the search query may be generated using a language other than SQL.
  • the search unit 47 searches for sentences that include expressions that are the same as or similar to the embedded expression of the word, based on the search query. For example, the search unit 47 uses the search query generated by the query generation unit 46 from data on the network to acquire a sentence that includes an expression corresponding to the search query.
  • the search unit 47 obtains posts that include expressions corresponding to the search query from, for example, SNS (Social Networking Service) data.
  • the search unit 47 may, for example, acquire a sentence that includes an expression corresponding to the search query from data on a network other than SNS.
  • FIG. 20 shows an example of a display screen that displays search results for expressions corresponding to the weighting coefficients of the feature quantities included in the objective function as a word cloud.
  • the example display screen in FIG. 20 displays expressions corresponding to combinations of weighting coefficients of feature amounts and similar expressions as a word cloud.
  • the number of occurrences of expressions similar to expressions corresponding to the combination of weighting coefficients of feature amounts in the search results is displayed using a word cloud method.
  • FIG. 21 shows an example of a display screen of search results of SNS posts that include expressions corresponding to weighting coefficients of feature quantities included in the objective function.
  • an SNS post that includes an expression corresponding to the weighting coefficient of the feature amount is displayed.
  • Mr. A is the candidate for the person who is close to the intention of the objective function currently being used.
  • the search unit 47 extracts, for example, Mr. A's posts on the SNS. Then, the output unit 41 outputs Mr. A's post to the user terminal device 20.
  • FIG. 22 shows an example of a display screen for selecting an intention to use for updating the objective function in the language space.
  • the update unit 43 uses the coefficient prediction model to update the weighting coefficient of the feature amount corresponding to the selected intention. Predict the combination of Then, the updating unit 43 updates the objective function using the combination of weighting coefficients of the feature quantities predicted by the coefficient prediction model.
  • FIG. 23 shows an example of a display screen where the intention used to update the objective function is input as text.
  • a sentence containing the intention is input into the input field on the left side.
  • the update unit 43 uses the coefficient prediction model to extract an expression corresponding to the intention from the sentence. A combination of weighting coefficients of features corresponding to the extracted expressions is predicted. Then, the updating unit 43 updates the objective function using the combination of weighting coefficients of the feature quantities predicted by the coefficient prediction model.
  • FIG. 24 is a diagram illustrating an example of an operation flow when the decision support system 40 updates the objective function.
  • the output unit 41 outputs the weighting coefficient of the feature quantity included in the objective function to be updated (step S41).
  • the output unit 41 outputs, to the user terminal device 20, for example, the weighting coefficient of the feature amount included in the objective function to be updated.
  • the receiving unit 42 receives an input of a change value of a weighting coefficient of a feature included in the objective function to be updated (step S42). For example, the receiving unit 42 acquires from the user terminal device 20 a change value of the weighting coefficient of the feature quantity, which is input to the user terminal device 20 by a user's operation.
  • the updating unit 43 updates the objective function using the changed value of the weighting coefficient of the feature quantity acquired by the accepting unit 42 (step S43). After updating the objective function, the updating unit 43 saves the updated objective function (step S44). The updating unit 43 stores the updated objective function in the storage unit 15, for example. The updated objective function is used by the determining unit 12 to determine the optimal solution.
  • FIG. 25 is a diagram illustrating an example of an operation flow when the decision support system 40 updates the objective function based on the embedded expression of words.
  • the output unit 41 outputs the embedded representation of the word corresponding to the weighting coefficient, which is used to update the objective function (step S51).
  • the output unit 41 outputs, for example, the embedded representation of the word corresponding to the weighting coefficient to the user terminal device 20.
  • the accepting unit 42 accepts input of the modified embedded expression (step S52).
  • the reception unit 42 acquires from the user terminal device 20 a modified embedded expression that is input to the user terminal device 20 by a user's operation.
  • the coefficient prediction unit 44 uses the coefficient prediction model to predict the combination of weighting coefficients of the feature amounts corresponding to the acquired embedded expression (step S53).
  • the updating unit 43 updates the objective function using the combination of weighting coefficients of the feature quantities predicted by the coefficient prediction unit 44 (step S54).
  • the updating unit 43 saves the updated objective function (step S55).
  • the updating unit 43 stores the updated objective function in, for example, the storage unit 15.
  • the updated objective function is used by the determining unit 12 to determine the optimal solution.
  • FIG. 26 is a diagram illustrating an example of an operation flow when the decision support system 40 extracts a sentence that includes an expression that corresponds to the weighting coefficient of the feature included in the objective function.
  • the expression prediction unit 45 uses the expression prediction model to predict an expression corresponding to the weighting coefficient of the feature included in the objective function used to determine the optimal solution (step S61).
  • the expression prediction unit 45 uses, for example, an expression prediction model that predicts an expression in language space from the weighting coefficients of the features included in the objective function, and calculates the features included in the objective function used to determine the optimal solution. Predict the embedded representation of the word corresponding to the weighting coefficient.
  • the query generation unit 46 generates a search query to search for sentences that include the corresponding expression (step S62).
  • the search unit 47 uses, for example, the search query generated by the query generation unit 46 to search the data on the network for a sentence corresponding to the search query (step S63).
  • the search unit 47 obtains posts corresponding to the search query from, for example, SNS data on the network.
  • the output unit 41 When a sentence including an expression corresponding to the search query is acquired, the output unit 41 outputs a search result using the search query (step S64). The output unit 41 outputs a search result using a search query to the user terminal device 20, for example.
  • the decision support system 40 obtains the change value of the weighting coefficient of the feature quantity included in the objective function.
  • the decision support system 40 then updates the objective function using the changed value.
  • the updated objective function for example, it is possible to determine an optimal solution that is optimized depending on the person who will use the optimal solution regarding prediction of race results.
  • the decision support system 40 When displaying the weighting coefficients of the feature quantities included in the objective function on a graph and accepting input of change values, the decision support system 40 changes the objective function based on the result of changing the display state of the weighting coefficients on the graph. Update. By updating the objective function in this way, optimization of the objective function becomes easier. By optimizing the objective function, it is possible to obtain an optimal solution that is more suitable for the person who uses the optimal solution in predicting the race result.
  • the user when updating the objective function based on the selection result of the intention corresponding to the objective function, the user can easily update the objective function even if the user does not have knowledge about changing the weighting coefficient of the feature amount.
  • the decision support system 40 extracts sentences that include expressions in the linguistic space that correspond to the weighting coefficients of the features included in the objective function.
  • the decision support system 40 then outputs the extracted text to the user terminal device 20, for example.
  • the decision support system 40 can output information related to the reason for determining the optimal solution.
  • the user of the decision support system 40 can refer to information related to the reason for determining the optimal solution and interpret the result of determining the optimal solution.
  • FIG. 27 is a diagram showing an example of the configuration of the decision support system 100 of this embodiment.
  • the decision support system 100 includes an acquisition section 101, a determination section 102, and an output section 103.
  • the acquisition unit 101 acquires information regarding races in publicly managed competitions.
  • the determining unit 102 uses an objective function generated in advance by inverse reinforcement learning based on the decision history regarding the prediction of race results in publicly managed competitions, and the obtained information regarding races in publicly managed competitions to determine the optimal solution for predicting race results. Determine.
  • the output unit 103 outputs information regarding prediction of the race result based on the determined optimal solution.
  • the acquisition unit 11 of the first embodiment is an example of the acquisition unit 101. Further, the acquisition unit 101 is one aspect of an acquisition means.
  • the determining unit 12 of the first embodiment is an example of the determining unit 102. Furthermore, the determining unit 102 is one aspect of determining means.
  • the output unit 13 of the first embodiment is an example of the output unit 103. Furthermore, the output unit 103 is one aspect of output means.
  • FIG. 28 is a diagram illustrating an example of the operation flow of the decision support system 100.
  • the acquisition unit 101 acquires information regarding races in publicly managed competitions (step S101).
  • the determining unit 102 uses an objective function generated in advance by inverse reinforcement learning based on the decision history regarding prediction of the race result in the publicly managed competition and the acquired information regarding the race in the publicly managed competition.
  • the optimal solution for predicting the race result is determined using the following (step S102). Once the optimal solution is determined, the output unit 103 outputs information regarding prediction of the race result based on the determined optimal solution (step S103).
  • the decision-making support system 100 of this embodiment uses an objective function generated in advance by inverse reinforcement learning based on the decision-making history regarding the prediction of race results in publicly managed competitions, and information regarding races in publicly managed competitions. Determine the optimal solution for prediction. The decision support system 100 then outputs the determined optimal solution. As a result, by using the decision-making support system 100, it is possible to easily make decisions regarding the race results of publicly managed competitions.
  • FIG. 29 shows a computer that executes a computer program that performs each process in the decision support system 10 of the first embodiment, the decision support system 40 of the second embodiment, and the decision support system 100 of the third embodiment.
  • 200 shows an example of a configuration of 200.
  • the computer 200 includes a CPU (Central Processing Unit) 201, a memory 202, a storage device 203, an input/output I/F (Interface) 204, and a communication I/F 205.
  • CPU Central Processing Unit
  • the CPU 201 reads computer programs for performing each process from the storage device 203 and executes them.
  • the CPU 201 may be configured by a combination of multiple CPUs. Further, the CPU 201 may be configured by a combination of a CPU and other types of processors. For example, the CPU 201 may be configured by a combination of a CPU and a GPU (Graphics Processing Unit).
  • the memory 202 is configured with a DRAM (Dynamic Random Access Memory) or the like, and temporarily stores computer programs executed by the CPU 201 and data being processed.
  • the storage device 203 stores computer programs executed by the CPU 201.
  • the storage device 203 is configured by, for example, a nonvolatile semiconductor storage device. Other storage devices such as a hard disk drive may be used as the storage device 203.
  • the input/output I/F 204 is an interface that receives input from a worker and outputs display data and the like.
  • the communication I/F 205 is an interface that transmits and receives data between the user terminal device 20 and the information management server 30. Further, the user terminal device 20 and the information management server 30 may also have similar configurations.
  • the computer program used to execute each process can also be stored and distributed in a computer-readable recording medium that non-temporarily records data.
  • a computer-readable recording medium for example, a magnetic tape for data recording or a magnetic disk such as a hard disk can be used.
  • an optical disc such as a CD-ROM (Compact Disc Read Only Memory) can also be used.
  • a nonvolatile semiconductor memory device may be used as the recording medium.
  • the output means outputs data visualizing combinations of weighting coefficients of features included in the objective function.
  • Decision support system according to appendix 1 or 2.
  • reception means for accepting a change value for changing a weighting coefficient of a feature included in the objective function; and updating means for updating the objective function using the received change value,
  • the determining means determines the optimal solution using the updated objective function.
  • the output means outputs weighting coefficients of features included in the objective function as a graph,
  • the receiving means receives the changed value input by changing a weighting coefficient of a feature included in the objective function displayed on the graph. Decision support system described in Appendix 4.
  • Coefficient prediction means for predicting the combination of weighting coefficients from the embedded representation of the word using a coefficient prediction model that predicts the combination of weighting coefficients of the feature quantities included in the objective function from the embedded representation of the word; further comprising: updating means for updating the objective function based on the combination of the weighting coefficients predicted by the coefficient predicting means; The decision support system described in any of Supplementary Notes 1 to 3.
  • a combination of weighting coefficients is calculated from a combination of weighting coefficients of features included in the objective function.
  • Decision Support System 11 Acquisition Unit 12 Determination Unit 13 Output Unit 14 Generation Unit 15 Storage Unit 20 User Terminal Device 30 Information Management Server 40 Decision Support System 41 Output Unit 42 Reception Unit 43 Update Unit 44 Coefficient Prediction Unit 45 Expression Prediction Section 46 Query generation section 47 Search section 100 Decision support system 101 Acquisition section 102 Determination section 103 Output section 200 Computer 201 CPU 202 Memory 203 Storage device 204 Input/output I/F 205 Communication I/F

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KR101976988B1 (ko) * 2018-06-21 2019-08-28 주식회사 미디어피아 경마예상 서비스를 제공하는 서버 및 방법
JP2020149601A (ja) * 2019-03-15 2020-09-17 エヌ・ティ・ティ・コミュニケーションズ株式会社 データ処理装置、データ処理方法及びデータ処理プログラム
WO2021229626A1 (ja) * 2020-05-11 2021-11-18 日本電気株式会社 学習装置、学習方法および学習プログラム

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2020060875A (ja) * 2018-10-05 2020-04-16 富士通株式会社 生成プログラム、生成方法、および、生成装置

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR101976988B1 (ko) * 2018-06-21 2019-08-28 주식회사 미디어피아 경마예상 서비스를 제공하는 서버 및 방법
JP2020149601A (ja) * 2019-03-15 2020-09-17 エヌ・ティ・ティ・コミュニケーションズ株式会社 データ処理装置、データ処理方法及びデータ処理プログラム
WO2021229626A1 (ja) * 2020-05-11 2021-11-18 日本電気株式会社 学習装置、学習方法および学習プログラム

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
TAKUMI YOSHIDA, YOKOYAMA SOICHIRO, YAMASHITA TOMOHISA, KAWAMURA HIDENOR: "Automatic Content Generation for Purchasing Support of Bicycle Tickets", 119TH KNOWLEDGE BASE SYSTEM STUDY GROUP MATERIALS (SIG-KBS-B903), 1 March 2020 (2020-03-01) - 10 March 2020 (2020-03-10), pages 17 - 22, XP093092599 *

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