Information processing device, optimization method, and control program

Abstract

The information processing device (1) is an information processing device that executes an optimization process using an objective function that indicates an evaluation value of a candidate solution, and is equipped with an optimization processing unit (12) that executes the optimization process based on a user's non-preference information.

Description

[Technical field]

[0001] The present disclosure relates to an information processing device, an optimization method, and a control program. [Background technology]

[0002] In recent years, a technique has been proposed for determining the content of a service to be provided to a service user in consideration of the user's preferences, interests, and the like (for example, Patent Document 1).

[0003] The technology described in Patent Document 1 accepts specification of conditions for the service to be provided from the user, and based on the specified conditions, searches for and proposes services that are closest to the user's preferences from among multiple candidates. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] JP 2022-96954 A Summary of the Invention [Problem to be solved by the invention]

[0005] According to the technology described in Patent Document 1, it is possible to identify and propose from among multiple candidates a proposal that is closest to the user's preferences based on specified conditions. However, the user needs to change the conditions and receive new proposals until a satisfactory proposal is made, and if the user cannot specify appropriate conditions, the number of repetitions of the series of processes increases. In other words, the technology described in Patent Document 1 has a problem in that it is not possible to efficiently search for a solution.

[0006] The present disclosure has been made in consideration of the above, and has an object to provide an information processing device that can reduce the number of interactions performed to obtain a solution. [Means for solving the problem]

[0007] In order to solve the above-mentioned problems and achieve the object, the present disclosure provides an information processing device that executes an optimization process using an objective function that indicates an evaluation value of a solution candidate, For selection of preferred solution candidates The system is characterized by comprising an optimization processing unit that executes optimization processing based on the user's non-preference information. Effect of the Invention

[0008] The information processing device according to the present disclosure has an effect of reducing the number of interactions required to obtain a solution. [Brief description of the drawings]

[0009] [Figure 1] FIG. 1 is a diagram showing a configuration example of an information processing device according to a first embodiment; [Diagram 2] FIG. 1 is a diagram showing an image of the overall operation of an information processing device according to a first embodiment; [Diagram 3] 1 is a flowchart showing an example of an operation of the information processing device according to the first embodiment. [Figure 4] FIG. 1 is a diagram illustrating a configuration example of an optimization processing unit included in an information processing device according to a first embodiment; [Diagram 5] FIG. 5 is a diagram showing an image of the overall operation of an information processing device including the optimization processing unit shown in FIG. 4; [Figure 6] FIG. 5 is a diagram showing another overall operation of the information processing device including the optimization processing unit shown in FIG. 4; [Figure 7] A flowchart showing an example of the operation of the optimization processing unit shown in FIG. [Figure 8] FIG. 5 is a diagram for explaining the operation of the optimization processing unit shown in FIG. 4; [Figure 9] A diagram showing an example of a utility function of preference information. [Figure 10] A diagram showing an example of a utility function for non-preference information [Figure 11] FIG. 13 is a diagram showing an image of an operation in which the information processing device according to the second embodiment optimizes the process of creating pairs of solution candidates based on the evaluation result of a decision maker. [Figure 12]FIG. 13 is a diagram showing an image of another operation in which the information processing device according to the second embodiment optimizes the process of creating pairs of solution candidates based on the evaluation result of the decision maker. [Figure 13] FIG. 13 is a diagram illustrating a configuration example of an optimization processing unit included in an information processing device according to a second embodiment. [Figure 14] FIG. 13 is a diagram showing a configuration example of another optimization processing unit included in the information processing device according to the second embodiment; [Figure 15] FIG. 13 is a diagram for explaining an optimization process executed by an information processing device according to a third embodiment. [Figure 16] A flowchart showing an example of an operation of an optimization processing unit of an information processing device according to a third embodiment. [Figure 17] FIG. 11 is a first diagram for explaining information used in an optimization process executed by an information processing device according to a fourth embodiment; [Figure 18] FIG. 2 is a second diagram for explaining information used in the optimization process executed by the information processing device according to the fourth embodiment; [Figure 19] FIG. 13 is a diagram showing a configuration example of an information processing device according to a fifth embodiment; [Figure 20] FIG. 13 is a diagram showing an example of preference conditions used by a preference proxy unit of the information processing device according to the fifth embodiment; [Figure 21] FIG. 23 is a diagram showing a configuration example of an information processing device according to a sixth embodiment; [Figure 22] FIG. 23 is a diagram showing an example of a screen displayed by an output unit of a user interface unit included in the information processing device according to the sixth embodiment; [Diagram 23] FIG. 1 is a diagram illustrating an example of hardware for implementing an information processing device according to a first embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0010] An information processing device, an optimization method, and a control program according to embodiments of the present disclosure will be described below in detail with reference to the accompanying drawings.

[0011] Embodiment 1 FIG. 1 is a diagram showing a configuration example of an information processing device 1 according to a first embodiment. The information processing device 1 derives an optimal solution that reflects a user's evaluation of a candidate solution. The information processing device 1 performs an optimization process to derive an optimal solution based on the user's evaluation result of the candidate solution. Here, the user is a decision maker in the optimization process, and the information processing device 1 performs the optimization process that reflects the will of the decision maker.

[0012] The information processing device 1 includes an information acquisition unit 11 that acquires information on the evaluation results of solution candidate data from a user, an optimization processing unit 12 that executes optimization processing, a memory unit 13 that stores information on solution candidate data to be presented to the user, and an output unit 14 that outputs the optimal solution derived by the optimization processing.

[0013] 2 is a diagram showing an image of the overall operation of the information processing device 1 according to the first embodiment. The information processing device 1 derives an optimal solution by repeating a series of operations, for example, presenting a pair of solution candidates to a user, acquiring an evaluation result that is a result of an evaluation by a decision maker of the presented pair of solution candidates, and performing an optimization process based on the acquired evaluation result to create a new pair of solution candidates and present it to the decision maker. Targets for which the information processing device 1 performs an optimization process to derive an optimal solution include, but are not limited to, for example, a meal menu, a combination of components to be mounted on an electronic device, an arrangement of components to be mounted on a board of an electronic device, and a layout of equipment to be installed in a facility such as a building.

[0014] In the example shown in Fig. 2, the information processing device 1 creates a plurality of pairs of candidate solutions to be compared and presents them to the decision maker, who selects a more preferable candidate solution for each of the presented pairs of candidate solutions. The information processing device 1 may also cause the decision maker to select a less preferable candidate solution. In a configuration in which the decision maker selects a more preferable candidate solution, information on the candidate solution selected by the decision maker becomes preferred information, and information on the candidate solution not selected by the decision maker becomes non-preferred information.

[0015] The information processing device 1 acquires the selection result by the decision maker as the evaluation result. The evaluation result includes preference information and non-preference information. In the example shown in FIG. 2, the information processing device 1 executes an optimization process using preference information and non-preference information. The preference information and non-preference information are temporarily stored in a preference information database (DB) and a non-preference information database, respectively, and then used in the optimization process. Details of the optimization process will be described later. The information processing device 1 may execute an optimization process using at least one of non-preference information and preference information, for example, an optimization process using non-preference information. When an optimization process using non-preference information is executed, the probability of creating pairs including solution candidates that do not match the decision maker's preferences is reduced, and pairs including solution candidates close to the optimal solution can be efficiently created, thereby shortening the time required to obtain the optimal solution. When a more preferable solution candidate is selected by the decision maker, it is assumed that the number of solution candidates that are not selected is greater than the number of solution candidates that are selected. An optimization process using non-preference information indicating candidate solutions that were not selected can avoid proceeding with the search in the direction of undesirable candidate solutions, and is expected to further reduce the number of times the process that requires the decision maker to select candidate solutions is executed compared to an optimization process using preference information.

[0016] FIG. 3 is a flowchart showing an example of the operation of the information processing device 1 according to the first embodiment.

[0017] First, the information processing device 1 acquires solution candidates (step S11). The information processing device 1 acquires multiple solution candidates. The solution candidates may be created by the information processing device 1 based on information prepared in advance. That is, the information processing device 1 may acquire multiple solution candidates created by other external devices from other devices, or may create multiple solution candidates based on prepared information. The information processing device 1 may perform optimization using an objective function for the acquired solution candidates. For example, when the solution candidates are meal menus, the menus may be optimized using protein, fat, carbohydrates, and the like as objective functions. The solution candidates are optimized using Bayesian optimization, genetic algorithms, and the like. Note that when acquiring multiple solution candidates created by other external devices, the information acquisition unit 11 acquires the solution candidates.

[0018] Next, the information processing device 1 acquires the evaluation result of the decision maker (step S12). Specifically, the information processing device 1 performs an interactive operation of combining two of the multiple solution candidates acquired in step S11 to create multiple pairs of solution candidates, presenting them to the decision maker, and accepting a selection of which of the multiple pairs is preferable, thereby acquiring an evaluation result for each of the presented solution candidates. The information processing device 1 may also accept a selection of which of the pairs is not preferable. The optimization processing unit 12 creates the pairs of solution candidates, and the output unit 14 presents the pairs of solution candidates to the decision maker. The information acquisition unit 11 acquires the evaluation result of the decision maker.

[0019] Next, the information processing device 1 performs an optimization process (step S13). Specifically, the optimization processing unit 12 performs an optimization process, which will be described later, based on the evaluation result acquired in step S12 as a process for deriving an optimal solution to be presented to a decision maker.

[0020] Next, the information processing device 1 judges whether or not to end the repetition (step S14). For example, the information processing device 1 judges that the repetition has ended when the number of repetitions reaches a predetermined number. The information processing device 1 may also judge that the repetition has ended when an operation instructing the end of the repetition is received from a decision maker.

[0021] If the information processing device 1 does not want to end the repetition (step S14: No), it executes steps S12 and S13 again. On the other hand, if the information processing device 1 wants to end the repetition (step S14: Yes), it outputs the optimal solution obtained by repeatedly executing steps S12 and S13, and ends the operation (step S15).

[0022] Next, the optimization process executed by the optimization processing unit 12 of the information processing device 1 will be described with reference to FIGS.

[0023] 4 is a diagram showing a configuration example of the optimization processing unit 12 included in the information processing device 1 according to the first embodiment. The optimization processing unit 12 includes a probability distribution creating unit 21 that creates a probability distribution of the preference information and non-preference information acquired by the information acquiring unit 11, a utility function creating unit 22 that creates a utility function by converting the probability distribution created by the probability distribution creating unit 21, an objective function updating unit 23 that updates an objective function indicating an evaluation value of a solution candidate based on the utility function created by the utility function creating unit 22, and a candidate extracting unit 24 that creates pairs of solution candidates based on the objective function. The probability distribution creating unit 21, the utility function creating unit 22, and the objective function updating unit 23 configure an objective function determining unit 20.

[0024] FIG. 5 is a diagram showing an image of the overall operation of the information processing device 1 including the optimization processing unit 12 shown in FIG. 4. The information processing device 1 having the optimization processing unit 12 shown in FIG. 4 creates a probability distribution of the preference information and non-preference information included in the evaluation result acquired from the decision maker, and converts the created probability distribution to create a utility function. Then, the information processing device 1 optimizes the solution candidates based on the utility function. The utility function indicates the evaluation value of the solution candidate presented to the decision maker. Note that the information processing device 1 having the optimization processing unit 12 shown in FIG. 4 may create a probability distribution of one of the preference information and non-preference information, for example, a probability distribution of non-preference information as shown in FIG. 6. FIG. 6 is a diagram showing another image of the overall operation of the information processing device 1 having the optimization processing unit 12 shown in FIG. 4.

[0025] Fig. 7 is a flowchart showing an example of the operation of the optimization processing unit 12 shown in Fig. 4. The optimization processing unit 12 optimizes solution candidates to be presented to a decision maker according to the flowchart shown in Fig. 7.

[0026] In the operation of optimizing solution candidates by the optimization processing unit 12 shown in Fig. 4, first, the probability distribution generating unit 21 generates a probability distribution based on the evaluation result obtained from the decision maker (step S21), and then the utility function generating unit 22 converts the probability distribution into a utility function (step S22). A specific example of the operations of these steps S21 and S22 will be described with reference to Fig. 8.

[0027] FIG. 8 is a diagram for explaining the operation of the optimization processing unit 12 shown in FIG. 4. FIG. 8 shows an example of creating a probability distribution and converting it into a utility function based on the evaluation results of each candidate staple food when the staple food candidates included in the meal menu are three types of rice, noodles, and bread. In the drawing, rice is described as "rice", noodles as "noodles", and bread as "bread". The same descriptions will be used in the following explanation. In the example shown in FIG. 8, a decision maker selects which of a plurality of prepared menu pairs is preferable, and as a result, the number of times that the menu with rice as the staple food was selected (preferred) is 8, the number of times that the menu with noodles as the staple food was selected is 5, and the number of times that the menu with bread as the staple food was selected is 1, while the number of times that the menu with rice as the staple food was not selected is 1, the number of times that the menu with noodles as the staple food was not selected is 5, and the number of times that the menu with bread as the staple food was not selected is 6. In this case, the preference information is rice=8, noodles=5, and bread=1, and the non-preference information is rice=1, noodles=5, and bread=6. Using this preference information and non-preference information, a probability distribution generating unit 21 generates a probability distribution for each of the preference information and non-preference information, and a utility function generating unit 22 converts the probability distribution for each of the preference information and non-preference information into a utility function.

[0028] The probability distribution creation unit 21 creates a probability distribution for each of the preference information and non-preference information using Bayesian estimation. In Bayesian estimation, a prior distribution for a certain hypothesis is set at first, and then the prior distribution is updated based on the likelihood of new data collected thereafter, thereby creating a posterior distribution, which is the final conclusion. Bayesian estimation is expressed by equation (1).

[0029]

number

[0030] In equation (1), p(θ i |D) is the posterior distribution, p(θ i ) is the prior distribution, p(D|θ i ) is the likelihood.

[0031] The probability distribution creation unit 21 creates a probability distribution based on equation (1). As an example, the creation of the probability distribution of the preference information shown in Fig. 8 will be described. The prior distribution p(θ) is expressed by equation (2).

[0032]

number

[0033] In the case of FIG. 8, the preference information is rice=8, noodles=5, and bread=1, and the data D is expressed by equation (3).

[0034]

number

[0035] In this case, the likelihood of rice, noodles, and bread is p(D|θ i ) becomes equation (4).

[0036]

number

[0037] The posterior distribution p(θ i |D) is expressed as the prior distribution p(θ) and the likelihood p(D|θ i ) and the posterior distribution p(θ i |D) is the probability distribution of preference information.

[0038]

number

[0039] The posterior distribution p(θ i If |D) is equation (5), for example, the posterior distribution p(θ rice |D) is expressed as equation (6).

[0040]

number

[0041] The probability distribution creation unit 21 creates the probability distribution of non-preferred information in a similar manner. As described with reference to FIG. 3, the optimization processing unit 12 repeatedly executes the optimization process. For this reason, when creating a probability distribution included in the repeatedly executed optimization process, the probability distribution created in the previous optimization process is used as the above-mentioned prior distribution p(θ i )

[0042] The above is the method for creating the probability distribution of the preference information for the main dish of the meal menu. The probability distribution creation unit 21 similarly creates the probability distribution of the preference information of the menu elements other than the main dish, and further creates the probability distribution P j is created according to equation (7).

[0043]

number

[0044] For example, if the menu candidate elements are rice, meat dishes, and tea, and the posterior distributions of the preference information for each are 8 / 14, 7 / 10, and 2 / 5, then the probability distribution P j =(8 / 14)·(7 / 10)·(2 / 5)=4 / 25.

[0045] The probability distribution generating unit 21 generates a probability distribution Q j For example, if the posterior distributions of the non-preference information for rice, meat dishes, and tea are 1 / 4, 2 / 7, and 2 / 5, then the probability distribution Q j =(1 / 4)·(2 / 7)·(2 / 5)=1 / 35.

[0046] The utility function creation unit 22 calculates the probability distribution P j and the probability distribution of non-preferred information Q j The utility function is a concept in economics that numerically expresses individual preferences. The utility function creation unit 22 converts the probability distribution Pj and the probability distribution of non-preferred information Q j Convert into a utility function.

[0047] For example, the utility function generating unit 22 may generate a probability distribution P j The utility function F p,j , and the probability distribution of non-preferred information Q j The utility function of non-preferred information F q,j Convert to.

[0048]

number

number

[0049] Figure 9 shows the utility function F p,j FIG. 10 shows an example of the utility function F q,j FIG. 9 is a diagram showing an example of τ p = 0.01, τ p = 1, τ p = 100, the utility function F p,j , and Fig. 10 shows an example of τ q = 0.01, τ q = 1, τ q = 100, the utility function F q,j Here is an example of τ p and τ q By adjusting the set value, the utility functions for preference information and non-preference information can be made to have slopes that match the characteristics of the decision maker. For example, it is possible to create a utility function that takes into account the characteristics of the decision maker (gambler, conservative, etc.), such as the more preferences there are, the more they are liked, and the more non-preferences there are, the more they are disliked.

[0050] The utility function generating unit 22 generates a utility function F using the preference information and the non-preference information. r,j is created according to equation (10).

[0051]

number

[0052] For example, the probability distribution P j P j = 4 / 25, probability distribution of non-preferred information Q j Q j = 1 / 35, the utility function F r,j =F p,j +F q,j =0.201+0.94=1.141.

[0053] Returning to the explanation of FIG. 7, after the utility function creation unit 22 creates a utility function by converting the probability distribution in step S22, the objective function update unit 23 optimizes the solution candidates based on the utility function (step S23). For example, the objective function update unit 23 updates the evaluation value of the solution candidate by setting the utility function created by the utility function creation unit 22 as a new objective function. The objective function update unit 23 may update the evaluation value of the solution candidate by setting the utility function as a first objective function and further optimizing the first objective function using Bayesian optimization, a genetic algorithm, or the like to update the first objective function to a second objective function. In updating the first objective function to the second objective function, one second objective function may be created by weighting and adding multiple first objective functions, or multiple second objective functions may be created by individually updating each of the multiple first objective functions.

[0054] Thus, in the optimization processing unit 12 shown in FIG. 4, the objective function determination unit 20 creates a probability distribution of preference information and a probability distribution of non-preference information based on the preference information and non-preference information acquired by the information acquisition unit 11, and further converts the probability distribution to create a utility function, and optimizes solution candidates based on the utility function.

[0055] In this embodiment, as a concrete example, the creation of probability distributions and utility functions for staple foods (rice, noodles, bread) included in a meal menu has been described, but the information processing device 1 also creates and optimizes probability distributions and utility functions in a similar manner for main dishes, side dishes, and other foods other than staple foods.

[0056] The candidate extraction unit 24 of the optimization processing unit 12 extracts a predetermined number of solution candidates in order of the highest evaluation value, and creates pairs of solution candidates by combining two of the extracted solution candidates. The candidate extraction unit 24 may randomly extract solution candidates from among the solution candidates with evaluation values ​​equal to or greater than a predetermined value to create pairs.

[0057] As described above, the information processing device 1 according to the first embodiment acquires the evaluation results of the solution candidates from the decision maker, and repeatedly executes an optimization process for updating an objective function indicating the evaluation values ​​of the solution candidates based on the evaluation results of the decision maker, to derive an optimal solution to be presented to the decision maker. Since the information processing device 1 according to the first embodiment executes an optimization process that reflects the evaluation results of the solution candidates by the decision maker, it is possible to reduce the number of interactions for acquiring the evaluation results of the solution candidates from the decision maker in order to derive an optimal solution.

[0058] Embodiment 2 In the first embodiment, an information processing device 1 that derives an optimal solution by optimizing an objective function that indicates an evaluation value of a candidate solution based on an evaluation result of a decision maker has been described. In contrast, an information processing device according to the second embodiment derives an optimal solution by optimizing a process for creating a pair of candidate solutions to be presented to a decision maker. The configuration of the information processing device according to the second embodiment is the same as that of the information processing device 1 according to the first embodiment shown in FIG. 1. However, the content of the optimization process executed by the optimization processing unit 12 is different from that of the first embodiment. Therefore, in this embodiment, the optimization process that is different from that of the first embodiment will be described, and the description of the other parts will be omitted.

[0059] FIG. 11 is a diagram showing an image of an operation in which the information processing device 1 according to the second embodiment optimizes the process of creating a pair of solution candidates based on the evaluation result of the decision maker. As shown in FIG. 11, the information processing device 1 optimizes the process of creating a pair of solution candidates based on preference information and non-preference information included in the evaluation result acquired from the decision maker. The information processing device 1 may optimize the process of creating a pair of solution candidates based on either the preference information or the non-preference information. For example, as shown in FIG. 12, the information processing device 1 may optimize the process of creating a pair of solution candidates based on non-preference information. FIG. 12 is a diagram showing an image of another operation in which the information processing device 1 according to the second embodiment optimizes the process of creating a pair of solution candidates based on the evaluation result of the decision maker.

[0060] The information processing device 1 that executes the operation shown in Fig. 11 updates, for example, an algorithm for extracting a candidate solution to be paired from a large number of prepared candidate solutions based on preference information and non-preference information. Fig. 13 shows a configuration example of an optimization processing unit 12a provided in the information processing device 1 that updates the algorithm for extracting candidate solutions by optimization processing. Fig. 13 is a diagram showing a configuration example of the optimization processing unit 12a provided in the information processing device 1 according to the second embodiment.

[0061] The optimization processing unit 12a has a configuration in which the objective function determination unit 20 of the optimization processing unit 12 shown in Fig. 4 described in the first embodiment is replaced with an algorithm updating unit 31. The algorithm updating unit 31 updates an algorithm for extracting solution candidates to be paired from a large number of solution candidates based on the evaluation results of the solution candidates by the decision maker. The algorithm updating unit 31 adjusts parameters of the algorithm, for example, so that a solution candidate selected many times by the decision maker is more likely to be extracted in the pair creation process, and a solution candidate selected few times by the decision maker is less likely to be extracted in the pair creation process.

[0062] The information processing device 1 may update the solution candidates based on the evaluation result of the decision maker. A configuration example of an optimization processing unit 12b provided in the information processing device 1 that updates the solution candidates through optimization processing is shown in Fig. 14. Fig. 14 is a diagram showing a configuration example of another optimization processing unit 12b provided in the information processing device 1 according to the second embodiment.

[0063] The optimization processing unit 12b is configured by replacing the objective function determination unit 20 of the optimization processing unit 12 shown in FIG. 4 described in the first embodiment with a candidate update unit 32. The candidate update unit 32 updates a large number of solution candidates that are the basis of pairs of solution candidates based on the evaluation results of the solution candidates by the decision maker. The candidate update unit 32 adds a new solution candidate that includes an element that constitutes a solution candidate that is selected frequently by the decision maker, for example. For example, in the optimization process for deriving an optimal solution for a meal menu described in the first embodiment, if the number of times a menu with rice as the staple food is selected is greater than the number of times other staple food menus are selected, the candidate update unit 32 adds the menu with rice as the staple food to the solution candidates. At this time, for example, if the number of times a menu with bread as the staple food is selected is less than the number of times other staple food menus are selected, a process of deleting some of the menus with bread as the staple food from the solution candidates and updating the solution candidates may be performed at the same time.

[0064] As described above, the information processing device 1 according to the second embodiment derives an optimal solution to be presented to a decision maker by repeatedly executing an optimization process for updating the process for creating pairs of solution candidates to be presented to the decision maker based on the evaluation result of the decision maker. This makes it possible to reduce the number of interactions for obtaining the evaluation result of the solution candidates from the decision maker, similar to the information processing device 1 according to the first embodiment.

[0065] Embodiment 3 In the first embodiment, an information processing device 1 is described that creates a probability distribution and converts it into a utility function for each of the preference information and non-preference information included in the evaluation result of the decision maker, and optimizes the objective function by Bayesian optimization or the like to derive an optimal solution. In the present embodiment, an information processing device is described that optimizes the objective function based on the preference information and non-preference information in a manner different from that of the first embodiment. The configuration of the information processing device according to the third embodiment is the same as that of the information processing device 1 according to the first embodiment shown in FIG. 1. However, the content of the optimization process executed by the optimization processing unit 12 is different from that of the first embodiment. For this reason, in the present embodiment, the optimization process different from that of the first embodiment is described, and the description of the other parts is omitted.

[0066] FIG. 15 is a diagram for explaining the optimization process executed by the information processing device 1 according to the third embodiment. As shown in FIG. 15, the information processing device 1 according to the third embodiment converts non-preference information registered in the non-preference information DB to create pseudo-preference information equivalent to preference information. The information processing device 1 derives an optimal solution based on the preference information and non-preference information. The conversion of non-preference information into pseudo-preference information is performed by the optimization processing unit 12 of the information processing device 1. The configuration of the optimization processing unit 12 of the information processing device 1 according to the third embodiment is similar to that of the optimization processing unit 12 of the first embodiment shown in FIG. 4.

[0067] Fig. 16 is a flowchart showing an example of the operation of the optimization processing unit 12 of the information processing device 1 according to the embodiment 3. The optimization processing unit 12 of the information processing device 1 according to the embodiment 3 optimizes solution candidates to be presented to a decision maker in accordance with the flowchart shown in Fig. 16.

[0068] In the optimization operation of the solution candidates by the optimization processing unit 12 of the information processing device 1 according to the third embodiment, first, the probability distribution creation unit 21 converts non-preference information into pseudo-preference information (step S31). Specifically, the probability distribution creation unit 21 converts the non-preference information into reciprocals to create the pseudo-preference information.

[0069] Next, the probability distribution creation unit 21 creates a probability distribution based on the preference information and the pseudo preference information (step S32). Specifically, the probability distribution creation unit 21 adds the pseudo preference information to the preference information, and creates a probability distribution for the preference information to which the pseudo preference information has been added. The information processing device 1 creates the probability distribution in the same manner as in the first embodiment.

[0070] Next, the utility function creation unit 22 converts the probability distribution into a utility function (step S33). The utility function creation unit 22 converts the probability distribution into a utility function in the same manner as in the first embodiment. For example, the utility function creation unit 22 converts the probability distribution of the preference information to which the pseudo preference information is added into a utility function F p,j The utility function creation unit 22 converts the utility function F q,j F q,j Set =1 and F r,j =F q,j +1 is output to the objective function update unit 23 as the utility function to be used in optimizing the solution candidates.

[0071] Next, the objective function update unit 23 optimizes the solution candidates based on the utility function input from the utility function creation unit 22 (step S34). The objective function update unit 23 optimizes the solution candidates in the same manner as in the first embodiment.

[0072] As described above, the information processing device 1 according to the third embodiment converts non-preference information included in the evaluation result of the solution candidates by the decision maker into pseudo-preference information equivalent to preference information, and optimizes the solution candidates based on the preference information and the pseudo-preference information. This makes it possible to obtain the same effects as the information processing device 1 according to the first embodiment.

[0073] In the present embodiment, an example has been described in which non-preference information is converted into a reciprocal to create pseudo-preference information equivalent to preference information, but the information processing device 1 may convert preference information into a reciprocal to create pseudo-non-preference information equivalent to non-preference information. That is, the information processing device 1 may create a utility function of non-preference information to which pseudo-non-preference information has been added based on non-preference information and pseudo-non-preference information, and optimize solution candidates. In this case, the method of creating the utility function and the method of optimizing solution candidates are the same as in the case of using preference information and pseudo-preference information.

[0074] Embodiment 4 Next, an information processing device according to the fourth embodiment will be described. The configuration of the information processing device according to the fourth embodiment is the same as that of the first embodiment, but the information used in the optimization process is different from that of the first embodiment. In the present embodiment, the parts different from the first embodiment will be described, and the description of the common parts will be omitted.

[0075] FIG. 17 is a first diagram for explaining information used in the optimization process executed by the information processing device 1 according to the fourth embodiment. As shown in FIG. 17, the information processing device 1 according to the fourth embodiment classifies non-preference information into indifference information and dislike information, and executes the optimization process using the preference information, indifference information, and dislike information. The indifference information is information that is not considered important when a decision maker evaluates solution candidates, and has little influence on the judgment of which of a pair of solution candidates is preferable. The dislike information is information that has a large influence on the judgment of which of a pair of solution candidates is not preferable when a decision maker evaluates solution candidates.

[0076] The process of classifying non-preference information into indifference information and dislike information is performed, for example, by the probability distribution creation unit 21 of the optimization processing unit 12. The probability distribution creation unit 21 classifies non-preference information registered in the non-preference information DB into indifference information and dislike information based on a comparison result between preference information registered in the preference information DB and non-preference information registered in the non-preference information DB. For example, when the solution candidate is a meal menu and the number of times a menu with noodles as a staple food is selected as a preferred menu is about the same as the number of times it is selected as an unpreferred menu (the number of times it was not selected as a preferred menu), the probability distribution creation unit 21 classifies noodles into indifference information. In addition, when the solution candidate is a meal menu and the number of times a menu with bread as a staple food is selected as a preferred menu is low and the number of times it is selected as an unpreferred menu is high, the probability distribution creation unit 21 classifies bread into dislike information. Similarly, main dishes, side dishes, etc. are classified into indifference information and dislike information.

[0077] After classifying the non-preference information into indifference information and dislike information, the optimization processing unit 12, for example, excludes the non-preference information corresponding to indifference information, and performs optimization processing using the non-preference information corresponding to dislike information and preference information. The optimization processing performed using the non-preference information corresponding to dislike information and preference information is the same as the optimization processing described in embodiment 1. In the optimization processing described in embodiment 2 or 3, the non-preference information and preference information corresponding to dislike information may be used.

[0078] After classifying the non-preference information into indifference information and dislike information, the optimization processing unit 12 may create a probability distribution for each of the preference information, indifference information and dislike information using the method described in embodiment 1, and create a utility function to be used in optimizing solution candidates based on each probability distribution for the preference information, indifference information and dislike information.

[0079] FIG. 18 is a second diagram for explaining information used in the optimization process executed by the information processing apparatus 1 according to Embodiment 4. As shown in FIG. 18, the information processing apparatus 1 may rank the preference information registered in the preference information DB and the non-preference information registered in the non-preference information DB. The ranking is performed for each element of the solution candidates. For example, when the solution candidate is a meal menu, ranking is performed for each element such as staple food, main dish, and side dish. The information processing apparatus 1, for example, assigns a score N1 to an element of a solution candidate that is considered preferable by the decision maker, and assigns a score N2 to an element of a solution candidate that is not considered preferable (N2 < N1), and the one with the higher total score is ranked higher. The information processing apparatus 1 classifies information with a lower rank as aversive information and information with a medium rank as indifferent information. After the classification is completed, the information processing apparatus 1 performs an optimization process using the preference information, indifferent information, and aversive information.

[0080] As described above, the information processing apparatus 1 according to Embodiment 4 classifies the non-preference information registered in the non-preference information DB into indifferent information and aversive information, and executes an optimization process using the preference information, indifferent information, and aversive information. Thereby, an optimization process that more specifically reflects the preference of the decision maker can be realized, and a further reduction in the number of dialogues for obtaining the evaluation result of the solution candidate from the decision maker can be expected.

[0081] Embodiment 5. FIG. 19 is a diagram showing a configuration example of the information processing apparatus 1a according to Embodiment 5. In FIG. 19, the same reference numerals as those in FIG. 1 are given to the components common to the information processing apparatus 1 according to Embodiment 1 shown in FIG. 1. In this embodiment, the description of the components denoted by the same reference numerals as those in FIG. 1 is omitted.

[0082] The information processing apparatus 1a includes an information acquisition unit 11, an optimization processing unit 12, a storage unit 13, an output unit 14, and a preference proxy unit 15. The information processing apparatus 1a has a configuration in which the preference proxy unit 15 is added to the information processing apparatus 1 according to Embodiments 1 to 4.

[0083] The preference proxy unit 15 determines, on behalf of the decision maker, which of the pairs of solution candidates created by the optimization processing unit 12 is to be preferred. The preference proxy unit 15 determines which of the pairs of solution candidates is to be preferred based on preset preference conditions. For example, the preference proxy unit 15 determines which of the pairs of solution candidates is to be preferred using preference conditions 501 shown in FIG. 20.

[0084] FIG. 20 is a diagram showing an example of the preference condition 501 used by the preference proxy unit 15 of the information processing device 1a according to the fifth embodiment. The preference condition 501 is used by the preference proxy unit 15 when the solution candidate is a meal menu. The preference condition 501 is composed of condition #1 and condition #2. Condition #1 indicates that the priority of the menu including "rice", "meat dish" and "salad" is higher than the other menus, and the priority of the menu including "bread", "fish dish" and "vinegared dish" is lower than the other menus. Condition #2 indicates that the priority of the menu including "rice" is higher than the priority of the menu including "noodles", the priority of the menu including "noodles" is higher than the priority of the menu including "bread", and the priority of the menu including "meat dish" is higher than the priority of the menu including "fish dish". More detailed conditions, such as the type of meat dish, the type of fish dish, the type of rice, the type of noodles, etc. may be set. The information processing device 1a is assumed to acquire and store the preference condition 501 from the decision maker in advance.

[0085] For example, if a pair of solution candidates is composed of a first menu including "rice", "meat dish" and "salad" and a second menu that is not a menu that includes "rice", "meat dish" and "salad", the preference proxy unit 15 prefers (selects) the first menu in accordance with condition #1 of the preference conditions 501. If a pair of solution candidates is composed of a first menu including "bread", "fish dish" and "vinegared dish" and a second menu that is not a menu that includes "bread", "fish dish" and "vinegared dish", the preference proxy unit 15 prefers the second menu in accordance with condition #1 of the preference conditions 501. If both the first menu and the second menu that constitute the pair of solution candidates are not a menu that includes "rice", "meat dish" and "salad" or a menu that includes "bread", "fish dish" and "vinegared dish", the preference proxy unit 15 determines the preferred menu based on condition #2 of the preference conditions 501.

[0086] As described above, the information processing device 1a according to the fifth embodiment is equipped with a preference proxy unit 15 that determines which of the candidate solutions is to be preferred on behalf of the decision maker, and therefore can execute the optimization process without dialogue with the decision maker, thereby reducing the burden on the decision maker.

[0087] Embodiment 6 Fig. 21 is a diagram showing a configuration example of an information processing device 1b according to the sixth embodiment. In Fig. 21, components common to the information processing device 1 according to the first embodiment shown in Fig. 1 are given the same reference numerals as in Fig. 1. In this embodiment, a description of components given the same reference numerals as in Fig. 1 will be omitted.

[0088] The information processing device 1b includes an information acquisition unit 11, an optimization processing unit 12, a storage unit 13, and an output unit 14. The information acquisition unit 11 and the output unit 14 constitute a user interface unit 16 that interacts with a decision maker.

[0089] The output unit 14 of the user interface unit 16 presents the pair of solution candidates created by the optimization processing unit 12 to the decision maker by displaying them on a display device (not shown). The information acquisition unit 11 of the user interface unit 16 acquires the decision maker's preference result for the pair of solution candidates presented by the output unit 14. When the solution candidates are meal menus, the user interface unit 16 presents two menu plans to the decision maker by displaying a screen shown in FIG. 22, for example. FIG. 22 is a diagram showing an example of a screen displayed by the output unit 14 of the user interface unit 16 included in the information processing device 1b according to the sixth embodiment. The screen shown in FIG. 22 includes a window 601 presenting menu plan 1, a window 602 presenting menu plan 2, and a group of operation buttons 610 for receiving an input of an evaluation result of the menu plan 1 and the menu plan 2 by the decision maker.

[0090] As described above, the information processing device 1b according to the sixth embodiment is provided with a user interface unit 16 that interacts with a decision maker to obtain evaluation results of candidate solutions, and can efficiently obtain preference information and non-preference information of the decision maker.

[0091] Next, the hardware for realizing the information processing devices 1, 1a, and 1b described in each embodiment will be described. Since the information processing devices 1, 1a, and 1b are realized by similar hardware, the hardware for realizing the information processing device 1 according to the first embodiment will be described as an example.

[0092] Fig. 23 is a diagram illustrating an example of hardware for implementing the information processing device 1 according to the first embodiment. Fig. 23 illustrates an example in which the information processing device 1 is implemented by a control circuit. The control circuit for implementing the information processing device 1 includes an input unit 91, a processor 92, a memory 93, and an output unit 94.

[0093] The input unit 91 receives a signal from the outside. The output unit 94 outputs a signal from the control circuit to the outside. The processor 92 is, for example, a CPU (Central Processing Unit), a central processing unit, a processing unit, an arithmetic unit, a microprocessor, a microcomputer, a DSP (Digital Signal Processor), etc. The memory 93 is, for example, a non-volatile or volatile semiconductor memory such as a RAM (Random Access Memory), a ROM (Read Only Memory), a flash memory, an EPROM (Erasable Programmable Read Only Memory), an EEPROM (Electrically Erasable Programmable Read Only Memory), a magnetic disk, a flexible disk, an optical disk, a compact disk, a mini disk, a DVD (Digital Versatile Disk), etc.

[0094] 23, a program for operating as the information acquisition unit 11 and optimization processing unit 12 of the information processing device 1 is stored in the memory 93, and the processor 92 reads and executes this program to realize the information acquisition unit 11 and optimization processing unit 12. The above-mentioned program stored in the memory 93 may be provided to a user or the like in a state written on a storage medium such as a CD (Compact Disc)-ROM or DVD-ROM, or may be provided via a network.

[0095] The storage unit 13 and the output unit 14 of the information processing device 1 are realized by a memory 93 and an output unit 94 of the control circuit, respectively.

[0096] The information processing device 1 can also be realized by dedicated hardware. The dedicated hardware for realizing the information processing device 1 can be, for example, a single circuit, a composite circuit, a programmed processor, a parallel programmed processor, an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or a combination of these.

[0097] The configurations shown in the above embodiments are merely examples, and may be combined with other known technologies, or the embodiments may be combined with each other, or part of the configuration may be omitted or modified without departing from the scope of the invention. For example, in each embodiment, the case where the object of optimization is a meal menu has been described, but the object of optimization may be the arrangement of components constituting an electronic device, the combination of components constituting an electronic device, or the like. [Explanation of symbols]

[0098] 1, 1a, 1b information processing device, 11 information acquisition unit, 12, 12a, 12b optimization processing unit, 13 memory unit, 14 output unit, 15 preference agent unit, 16 user interface unit, 20 objective function determination unit, 21 probability distribution creation unit, 22 utility function creation unit, 23 objective function update unit, 24 candidate extraction unit, 31 algorithm update unit, 32 candidate update unit.

Claims

1. An information processing device that performs an optimization process using an objective function that shows the evaluation value of candidate solutions, An optimization processing unit that performs the optimization process based on the user's non-preference information in order to select a preferred candidate solution. An information processing device characterized by comprising:

2. Based on the aforementioned non-preference information, an objective function determination unit converts the first objective function representing the evaluation value into a second objective function. Equipped with, The optimization processing unit performs the optimization process using the second objective function. The information processing apparatus according to feature 1.

3. The objective function determination unit converts the first objective function to the second objective function using a utility function created based on the non-preference information. The information processing apparatus according to feature 2.

4. The objective function determination unit transforms the probability distribution of the non-preference information to create the utility function. The information processing apparatus according to claim 3.

5. Information acquisition unit that acquires the aforementioned non-preference information, Equipped with, The objective function determination unit, after creating a first utility function by transforming the probability distribution of the first non-preference information, updates the probability distribution based on the second non-preference information when the information acquisition unit acquires second non-preference information, and updates the objective function based on the second utility function obtained by transforming the updated probability distribution. The information processing apparatus according to feature 4.

6. The optimization processing unit determines candidate solutions to present to the user based on the objective function and the non-preference information. The information processing apparatus according to feature 1.

7. The optimization processing unit described above, A candidate extraction unit extracts candidate solutions to present to the user from among a plurality of candidate solutions based on the aforementioned objective function, The candidate extraction unit updates the algorithm used for extraction based on the non-preference information, The information processing apparatus according to claim 6, characterized by comprising:

8. The optimization processing unit described above, A candidate extraction unit extracts candidate solutions to present to the user from among a plurality of candidate solutions based on the aforementioned objective function, A candidate update unit updates a plurality of candidate solutions that are the target of extraction by the candidate extraction unit based on the non-preference information, The information processing apparatus according to claim 6, characterized by comprising:

9. The optimization processing unit converts the preference information, which indicates candidate solutions preferred by the user, into its reciprocal to create pseudo-dislike information equivalent to the dislike information, and executes the optimization process based on the dislike information and the pseudo-dislike information. The information processing apparatus according to any one of features 1 to 8.

10. The optimization processing unit performs optimization processing based on the user's preference information and the disliked information. The information processing apparatus according to feature 1.

11. An information acquisition unit that acquires the preference information and the dispreference information, An objective function determination unit that transforms the first objective function representing the evaluation value into the second objective function based on a first utility function created by transforming the probability distribution of the preference information and a second utility function created by transforming the probability distribution of the disliked information, and determines the second utility function to be the objective function used in the optimization process, Equipped with, The objective function determination unit, After determining the objective function based on the first preference information and the first dispreference information, if the information acquisition unit acquires either the second preference information or the second dispreference information, the objective function is updated based on the second preference information or the second dispreference information acquired by the information acquisition unit. If the information acquisition unit acquires both the second preference information and the second dispreference information, the objective function is updated based on the second preference information and the second dispreference information acquired by the information acquisition unit. The information processing apparatus according to feature 10.

12. The optimization processing unit determines candidate solutions to present to the user based on the objective function, the non-preference information, and the preference information. The information processing apparatus according to feature 10.

13. The optimization processing unit converts the non-preference information into its reciprocal to create pseudo-preference information equivalent to the preference information, and executes the optimization process based on the preference information and the pseudo-preference information. The information processing apparatus according to feature 10.

14. Each of the candidate solutions includes multiple elements that are subject to the user's preferences, The optimization processing unit, based on the user's preference for the candidate solutions, classifies each of the multiple elements included in the candidate solutions that were not preferred by the user into elements that the user dislikes and elements that the user is of little interest to, and uses the information indicating the elements that the user dislikes as the non-preference information. An information processing apparatus according to any one of the features described in 10 to 13.

15. Each of the candidate solutions includes multiple elements that are subject to the user's preferences, The optimization processing unit, based on the user's preference for the candidate solutions, classifies each of the multiple elements included in the candidate solutions that were not preferred by the user into elements that the user dislikes and elements that the user is of little interest to, and executes the optimization process based on the dislike information indicating the elements that the user dislikes, the indifference information indicating the elements that the user is of little interest to, and the preference information. The information processing apparatus according to feature 10.

16. When candidate solutions are presented, a preference proxy unit performs the necessary selection of the candidate solutions on behalf of the user, based on the conditions under which the user prefers each of the presented candidate solutions, for the optimization process. The information processing apparatus according to claim 1 or 10, characterized by comprising:

17. A user interface unit that presents candidate solutions to the user and receives the user's preference for the presented candidate solutions. The information processing apparatus according to claim 1 or 10, characterized by comprising:

18. An optimization method in which an information processing device performs an optimization process using an objective function that indicates the evaluation value of candidate solutions, The optimization process is performed based on the user's non-preference information in order to select a preferred solution candidate. An optimization method characterized by the following:

19. A control program for an information processing device that performs an optimization process using an objective function that indicates the evaluation value of candidate solutions, The information processing device is made to perform the optimization process based on the user's non-preference information in order to select a preferred candidate solution. A control program characterized by the following features.

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