Visualization method, visualization device, and program

Abstract

This visualization device comprises a coefficient acquisition unit and an output control unit. The coefficient acquisition unit acquires, for each of a plurality of different objective functions, a weight coefficient for each of a plurality of feature amounts. The output control unit outputs, in a comparable manner, the acquired weight coefficients for the plurality of objective functions. The weight coefficients for the plurality of feature amounts express the degree of influence of the feature amounts on solutions to the plurality of objective functions.

Description

[Technical Field]

[0001] The present disclosure relates to a visualization method and the like. [Background technology]

[0002] In an optimization problem, the function that we want to maximize or minimize under given constraints is called the objective function.

[0003] Furthermore, with regard to machine learning technology, various models such as a logistic regression model, a random forest model, and a tree model can be adopted (see, for example, Patent Document 1).

[0004] Furthermore, there is a technology for learning an objective function based on the decision-making history of a subject (see, for example, Patent Document 2). [Prior art documents] [Patent documents]

[0005] [Patent Document 1] Japanese Patent Application Publication No. 2018-010458 [Patent Document 2] International Publication No. 2021 / 130916 Summary of the Invention [Problem to be solved by the invention]

[0006] When an objective function is obtained, it is difficult to understand which viewpoints have a strong influence on the objective function.

[0007] An example of an objective of the present disclosure is to provide a visualization method or the like that improves the ease of checking an objective function. [Means for solving the problem]

[0008] A visualization method according to one aspect of the present disclosure includes obtaining a weighting coefficient for each of a plurality of feature quantities for each of a plurality of different objective functions, and outputting the weighting coefficient obtained for each of the plurality of objective functions in a comparable manner, wherein the plurality of objective functions optimize the same behavior, and the weighting coefficient for each of the plurality of feature quantities represents the degree to which the feature quantity influences a solution for each of the plurality of objective functions.

[0009] A visualization device in one aspect of the present disclosure includes a coefficient acquisition means for acquiring a weighting coefficient for each of a plurality of feature quantities for each of a plurality of different objective functions, and an output control means for outputting the weighting coefficient acquired for each of the plurality of objective functions in a comparable manner, wherein the plurality of objective functions optimize the same behavior, and the weighting coefficient for each of the plurality of feature quantities represents the degree to which the feature quantity influences the solution of each of the plurality of objective functions.

[0010] A program in one aspect of the present disclosure causes a computer to execute processing to obtain a weighting coefficient for each of a plurality of feature quantities for each of a plurality of different objective functions, and to output the weighting coefficient obtained for each of the plurality of objective functions in a comparable manner, wherein the plurality of objective functions optimize the same behavior, and the weighting coefficient for each of the plurality of feature quantities represents the degree to which the feature quantity influences a solution for each of the plurality of objective functions.

[0011] The program may be stored in a non-transitory computer-readable recording medium. [Effects of the Invention]

[0012] According to the present disclosure, it is possible to improve the ease of checking the objective function. [Brief explanation of the drawings]

[0013] [Figure 1] 1 is a block diagram showing an example of a configuration of a visualization device according to a first embodiment. [Figure 2]4 is a flowchart illustrating an example of an operation of the visualization device according to the first embodiment. [Figure 3] FIG. 10 is a block diagram showing a configuration example of a visualization device according to a second embodiment. [Figure 4] FIG. 10 is an explanatory diagram showing an example of a screen in which weighting coefficients of feature amounts in a trade-off relationship are displayed adjacent to each other; [Figure 5] 10 is a flowchart showing a first operation example of the visualization device according to the first explanatory example. [Figure 6] 10 is an example of a screen displaying differences in weighting coefficients in a plurality of objective functions. [Figure 7] 10 is a flowchart showing a second operational example of the visualization device according to the first explanatory example. [Figure 8] FIG. 10 is an explanatory diagram showing an example of an objective function and weighting coefficients obtained from learning examples for each expert. [Figure 9] FIG. 10 is an explanatory diagram showing an example of a screen displaying a comparison of weighting coefficients for each objective function and the optimization results of job assignments for each persona. [Figure 10] 10 is a flowchart showing an example of an operation of the visualization device according to the second explanatory example. [Figure 11] FIG. 2 is an explanatory diagram illustrating an example of the hardware configuration of a computer. DETAILED DESCRIPTION OF THE INVENTION

[0014] Hereinafter, with reference to the drawings, embodiments of a visualization method, a visualization device, a program, and a non-transitory recording medium for recording the program according to the present disclosure will be described in detail. The disclosed technology is not limited to these embodiments.

[0015] First, an optimization problem is to find a solution that maximizes or minimizes a certain objective function under given constraints. Here, in each embodiment, the objective function uses features that evaluate the merits of the optimization target. The weighting of these features may be set based on experience or may be obtained by learning based on the decision-making history of the target.

[0016] For example, when creating an optimal shift schedule, features such as "labor costs" and "degree of reflection of vacation requests" are considered as objective function features. These features are weighted. In such a case, the intention of the shift schedule creator is to "create a shift schedule with as low labor costs as possible" and "create a shift schedule that accommodates vacation requests as much as possible."

[0017] The subject may be an expert or the like. There may be multiple subjects. Constraints are items that must be observed when making a decision. Features, i.e., viewpoints, are items that the subject takes into consideration when making a decision.

[0018] (Embodiment 1) First, in the first embodiment, basic functions of the visualization device will be described. FIG. 1 is a block diagram showing an example of the configuration of the visualization device according to the first embodiment. For example, a user performs optimization using a learned objective function, but needs to select an objective function that suits the user's intention. Therefore, the visualization device 10 visualizes the target user's intention in the objective function. In FIG. 1, the visualization device 10 includes a coefficient acquisition unit 101 and an output control unit 102.

[0019] The coefficient acquisition unit 101 acquires, for each of a plurality of different objective functions, a weighting coefficient for each of a plurality of feature quantities common to the plurality of objective functions. The plurality of feature quantities may be common to the plurality of objective functions. Alternatively, at least some of the plurality of feature quantities may be feature quantities present in some of the plurality of objective functions. The plurality of objective functions are criteria for deriving an optimal solution for the same behavior. For example, the plurality of objective functions may be obtained by learning based on a plurality of different decision-making histories. This learning may be, for example, inverse reinforcement learning. For example, there may be a decision-making history for each subject (e.g., an expert), or there may be a decision-making history for the same subject at different times, such as different time periods.

[0020] Furthermore, the term "behavior" as used herein includes, for example, work. In the following description, the term "work" may be used to describe the behavior. For example, the behavior may be, but is not limited to, a task of deciding the order of work, a scheduling task such as allocating shifts, a matching task such as assigning tasks, or a task of deciding a combination of dishes, such as allocating resources to combine dishes within a calorie limit.

[0021] Here, a weighting coefficient is assigned to each feature. The weighting coefficient of each feature represents the degree to which the feature affects the solution of each objective function. In other words, different feature values ​​are emphasized for each objective function, and the weighting coefficient represents, for example, which feature value is emphasized in each objective function. In this embodiment, the larger the weighting coefficient, the more important a feature value is in the objective function. For example, if a feature value is present in some of the objective functions but not in others, the weighting coefficient of this feature value in the other objective functions may be 0, i.e., the feature value is not emphasized.

[0022] The output control unit 102 outputs a weighting coefficient for each of a plurality of feature quantities for each of a plurality of objective functions in a comparable manner. The output format of the output control unit 102 is not particularly limited. The output control unit 102 may display each weighting coefficient on a display device, or may output the weighting coefficient as audio to an audio output device. An output device such as a display device or an audio output device may be provided in the visualization device 10, or may be provided in a device connected to the visualization device 10 via a communication network or the like.

[0023] Furthermore, for example, the output control unit 102 may output weighting coefficients for each of a plurality of feature quantities for each of a plurality of objective functions. The order in which the weighting coefficients are arranged is not particularly limited. For example, the output control unit 102 may arrange the weighting coefficients in a predetermined order or in a specified order. As will be described in detail in the second embodiment, the output control unit 102 may output weighting coefficients arranged so that feature quantities in a trade-off relationship are adjacent to each other.

[0024] Furthermore, for example, the output control unit 102 may graph the weighting coefficients of each of the multiple feature quantities and output the graph. The type of graph is not particularly limited, and may be a bar graph, a pie chart, a band graph, or the like. The order in which the weighting coefficients are arranged when graphed is not particularly limited. For example, the order in which the weighting coefficients are arranged when graphed may be the order described above.

[0025] 2 is a flowchart showing an example of an operation of the visualization device 10 according to the first embodiment. The visualization device 10 acquires a weighting coefficient of each feature amount for each objective function (step S101). The visualization device 10 outputs the weighting coefficient of each feature amount for each objective function in a comparable manner (step S102).

[0026] As described above, in the first embodiment, the visualization device 10 acquires a weighting coefficient for each feature amount for each objective function and outputs the acquired weighting coefficients in a comparable manner. This visualizes the intention based on which the objective function was learned or set. Therefore, it is possible to improve the ease of checking the objective function. In other words, the intention of each objective function is displayed in a format that is easier for the user to understand. This allows the user to get a sense of satisfaction with the intention based on which a solution obtained by an objective function was obtained, for example. Note that at least some of the multiple objective functions may be objective functions obtained by multi-objective optimization.

[0027] The first embodiment is not limited to the above-described examples and can be modified in various ways. Furthermore, each functional unit may be realized by a single device. For example, each functional unit may be realized by a single device, such as a single server or a single terminal device that can be operated by a user. Alternatively, each functional unit may be realized by a plurality of devices, such as a visualization system.

[0028] (Embodiment 2) Next, a detailed description of the second embodiment will be given with reference to the drawings. Below, the description of the second embodiment will be omitted for the sake of clarity, as long as it does not make the description of the second embodiment unclear.

[0029] 3 is a block diagram showing an example of a configuration of a visualization device according to the second embodiment. The visualization device 20 includes a coefficient acquisition unit 201, an output control unit 202, a relationship information acquisition unit 203, and a solution acquisition unit 204. In the second embodiment, the relationship information acquisition unit 203 and the solution acquisition unit 204 are added to the first embodiment. The coefficient acquisition unit 201 and the output control unit 202 have the basic functions of the coefficient acquisition unit 101 and the output control unit 102 described in the first embodiment, respectively. Furthermore, for example, the visualization device 20 may have information on a plurality of objective functions.

[0030] As described in the first embodiment, the coefficient acquisition unit 201 acquires a weighting coefficient for each of a plurality of feature quantities for each objective function.

[0031] As explained in the first embodiment, the output control unit 202 outputs the weighting coefficients acquired for each of the plurality of objective functions in a comparable manner.

[0032] In order to explain the relationship information acquisition unit 203, the solution acquisition unit 204, and the output control unit 202 in more detail, an explanation example 1 and an explanation example 2 will be used.

[0033] (Example 1) In the first explanatory example, an example in which the weighting coefficients of the feature quantities are displayed in a graph will be shown. Here, examples in which a graph of the weighting coefficients is displayed will be described using the first and second examples.

[0034] <Example 1> Example 1 shows weighting coefficients for features that are in a trade-off relationship for each objective function, displayed adjacent to each other. For example, suppose an objective function for an optimization problem that solves the task of scheduling store staff shifts in store operations has been learned. In cases where low labor costs are preferable but a large number of store staff are desired, there is a trade-off between the features related to labor costs and the features related to securing staff.

[0035] The relationship information acquisition unit 203 acquires relationship information representing feature quantities that are in a trade-off relationship among a plurality of feature quantities. Whether at least two feature quantities are in a trade-off relationship may be determined manually or based on whether predetermined conditions are met. In other words, the relationship information may be created manually or based on whether predetermined conditions are met.

[0036] The output control unit 202 outputs, for each of the plurality of objective functions based on the relationship information, the weighting coefficients of the plurality of feature quantities arranged so that the weighting coefficients of the feature quantities in a trade-off relationship among the plurality of feature quantities are adjacent to each other. For example, the output control unit 202 may graph the weighting coefficients of the plurality of feature quantities so that the weighting coefficients of the feature quantities in a trade-off relationship are adjacent to each other.

[0037] Furthermore, the output control unit 202 may output information indicating that there is a trade-off relationship between different feature quantities. For example, the information indicating the trade-off relationship may be a symbol such as an arrow, a number, a letter, or a color.

[0038] Furthermore, for example, when the difference between the weighting coefficients of feature amounts in a trade-off relationship is equal to or greater than a threshold, the output control unit 202 may output information indicating that the difference is equal to or greater than the threshold.

[0039] Fig. 4 is an explanatory diagram showing an example of a screen in which weighting coefficients of feature quantities that are in a trade-off relationship are displayed adjacent to each other. Fig. 4 shows a graph that allows comparison of the weighting coefficients of feature quantities for each objective function. In each graph, the horizontal axis represents each feature quantity, and the vertical axis represents the weighting coefficient.

[0040] For example, in Fig. 4, the double-headed arrows indicate that the features are in a trade-off relationship. In Fig. 4, the feature A and the feature D are in a trade-off relationship. Also, in Fig. 4, the feature C and the feature F are in a trade-off relationship.

[0041] For example, consider a case where feature A is labor costs and feature D is securing personnel. In FIG. 4, in objective function X, the weighting coefficient of feature A is smaller than the weighting coefficient of feature D. In contrast, in FIG. 4, in objective function Y, the weighting coefficient of feature D is smaller than the weighting coefficient of feature A. For this reason, for example, a user who places more importance on labor costs can select objective function X, and a user who places more importance on securing personnel can select objective function Y.

[0042] Furthermore, for example, when the difference between the weighting coefficients of multiple different feature quantities in a trade-off relationship is equal to or greater than a threshold, the output control unit 202 outputs information indicating that the difference is equal to or greater than a threshold. The threshold may be predetermined and is not particularly limited. For example, when the difference is equal to or greater than a threshold, the output control unit 202 may highlight the multiple different feature quantities in a trade-off relationship. In FIG. 4, information indicating that the difference is equal to or greater than a threshold is represented by a dotted line box. For example, in FIG. 4, in the graph of objective function X, the difference between the weighting coefficient of feature quantity A and the weighting coefficient of feature quantity D is equal to or greater than the threshold, so the weighting coefficients of feature quantity A and feature quantity D are highlighted by being surrounded by a dotted line. For example, in the graph of objective function Y, the difference between the weighting coefficient of feature quantity C and the weighting coefficient of feature quantity F is equal to or greater than a threshold, so the weighting coefficients of feature quantity C and feature quantity F are highlighted by being surrounded by a dotted line.

[0043] 5 is a flowchart showing an operation example 1 of the visualization device 20 according to the explanatory example 1. The visualization device 20 acquires a weighting coefficient of a feature for each objective function (step S201). Next, the relationship information acquisition unit 203 acquires relationship information representing feature quantities in a trade-off relationship among a plurality of feature quantities (step S202). Next, the output control unit 202 causes the display device to display, for each objective function, the weighting coefficients of the feature quantities in a trade-off relationship so as to be adjacent to each other (step S203).

[0044] <Example 2> Example 2 describes an example in which the difference between the weighting coefficients for each of a plurality of feature quantities in a plurality of objective functions is displayed. Note that Example 2 may be combined with Example 1.

[0045] For example, the output control unit 202 outputs the difference between the weighting coefficients for each of a plurality of feature quantities in a plurality of objective functions. Taking objective function X and objective function Y as an example, with feature quantity A, the output control unit 202 outputs the difference between the weighting coefficient for feature quantity A of objective function X and the weighting coefficient for feature quantity A of objective function Y.

[0046] Furthermore, since there are multiple feature amounts, the output control unit 202 may output the differences in the weighting coefficients of the multiple feature amounts side by side. In combination with Example 1, the output control unit 202 may display the differences in the weighting coefficients of feature amounts that are in a trade-off relationship side by side. For example, the output control unit 202 may output the differences in the weighting coefficients of the multiple feature amounts in a graph. In combination with Example 1, the output control unit 202 may output the differences in the weighting coefficients of feature amounts that are in a trade-off relationship side by side.

[0047] Figure 6 is an example of a screen displaying the differences in weighting coefficients for multiple objective functions. Figure 6 also shows a graph that allows for comparison of the differences in weighting coefficients for feature quantities for multiple objective functions. In each graph, the horizontal axis represents each feature quantity, and the vertical axis represents the difference in weighting coefficients.

[0048] FIG. 6 shows the difference in feature amounts between objective function X and objective function Y. The vertical axis is, for example, the value (difference) obtained by subtracting the weight coefficient of objective function Y from the weight coefficient of objective function X. In FIG. 6, the greater the weight coefficient of objective function X is compared to the weight coefficient of objective function Y, the more positive the difference becomes. The smaller the weight coefficient of objective function X is compared to the weight coefficient of objective function Y, the more negative the difference becomes.

[0049] 6, for example, similar to FIG. 4, the output control unit 202 may output adjacent differences between feature amounts that are in a trade-off relationship. In FIG. 6, a double-headed arrow indicates that the feature amounts are in a trade-off relationship. In FIG. 6, feature amount A and feature amount D are in a trade-off relationship. Also, in FIG. 6, feature amount C and feature amount F are in a trade-off relationship.

[0050] 7 is a flowchart showing an operation example 2 of the visualization device 20 according to the explanatory example 1. The coefficient acquisition unit 201 acquires a weighting coefficient of a feature for each objective function (step S211). Next, the relationship information acquisition unit 203 acquires relationship information representing feature quantities in a trade-off relationship among a plurality of feature quantities (step S212). Next, the output control unit 202 displays the feature quantities in a trade-off relationship on the display device, arranging them so that the differences in the weighting coefficients of the feature quantities in the trade-off relationship are adjacent to each other (step S213).

[0051] (Example 2) In the second explanatory example, weighting coefficients for each of a plurality of objective functions are presented in a comparable manner, and the results of actually solving an optimization problem using the objective functions are presented for each objective function.

[0052] Fig. 8 is an explanatory diagram showing an example of the objective function and weighting coefficients obtained from the learning examples for each expert. Fig. 8 shows the feature values ​​of the objective function and the weighting coefficients of the feature values ​​for optimizing the work to be assigned.

[0053] In Figure 8, we will take an example of obtaining objective functions for experts XX and YY as training examples. When XX is used as a training example, objective function ZX is obtained, and when YY is used as a training example, objective function ZY is obtained.

[0054] For example, in Figure 8, the feature quantities include "match with career aspirations," "family circumstances," "match with experience," and "match with personality." Objective function ZX and objective function ZY each have the same feature quantities, but the weighting coefficients of the feature quantities are different. In Figure 8, objective function ZX has a lower weighting coefficient value for the feature quantity "family circumstances" than objective function ZY, and a higher weighting coefficient value for the feature quantity "match with experience." Therefore, objective function ZX places more importance on "match with experience" than objective function ZY. On the other hand, objective function ZY places more importance on "family circumstances" than objective function ZX.

[0055] The solution acquisition unit 204 acquires a solution obtained based on each of the objective functions to which information representing a predetermined state is given for the objective function. Here, the obtained solution will be described using an optimal solution as an example, but it may also be a feasible solution and is not particularly limited. The predetermined state may be, for example, a representative state. For example, the representative state may be, for example, a state specified by a user. The representative state may be a state in which the user is in a certain state or a state empirically created by a user. Furthermore, the representative state is a state on which judgments differ among experts. The specific state may be determined based on the task to be optimized.

[0056] Here, the data used to calculate the feature quantities of the objective function is state data, and a typical state thereof is a predetermined state, for example, a representative state. By using the objective function and the representative state, an optimal solution in the representative state is derived. Therefore, data related to the calculation of the feature quantities of the objective function is used as information representing the predetermined state.

[0057] The information representing the representative state may be sales performance data for each product. The information representing the representative state may also be campaign data such as discounts, discounts for the sale of a combination of products, etc. The information representing the representative state may also be environmental data related to the environment such as weather, temperature, humidity, etc., or calendar data such as days of the week, holidays, summer vacation, etc.

[0058] Furthermore, the information representing a representative state may be event data such as nearby events or event dates and times. The information representing a representative state may be inventory data such as deliveries and disposals. The information representing a representative state may be persona data such as personas that represent the state of a person, such as employee skills, experience, and desired careers. The information representing a representative state may also be data such as a shift preference list. The information representing a representative state may also be forecast data such as expected workload.

[0059] For example, here is an example of a typical state when environmental data is used as state data. If the environmental data is "weather, temperature, humidity," a typical state would be something like "sunny, temperature 30 degrees, humidity 20%," and vector data representing these values ​​is information that represents the typical state.

[0060] For example, in the case of optimizing an ordering operation, information representing a typical state may be environmental data, calendar data, event data, sales forecast values, current inventory levels, back-office margins, etc. For example, in the case of optimizing a task of allocating work, persona data may be used as information representing a typical state.

[0061] Here, in the objective function with weighting coefficients as shown in Figure 8, when calculating the "degree of match with career aspirations," data on "person's career aspirations" and "task-related careers" are used as information representing a representative state.

[0062] Here, Mr. AA and Mr. B B The desired career type and the degree of match between the career type and the task are shown below. For example, assume that there are career types from a to d. The desired career type is set to 1. Also, if the task and career type match, the degree of match is set to 1. Carrier Type abcd AA 0 1 0 0 BB 0 0 1 0 Task 0 1 0 0 In this example, AA's desired career type is b, and BB's desired career type is c. Furthermore, for example, the degree of match between career type a and the task is 0. The degree of match between career type b and the task is 1. The degree of match between career type c and the task is 0. The degree of match between career type d and the task is 0. In such a case, the degree of match between the task and AA's career aspirations is 1, and the degree of match between the task and BB's career aspirations is 0. In such a case, whether to ultimately assign the task to AA or BB, taking into account other feature quantities (for example, match with experience), depends on the weighting of which feature quantities are emphasized. In the present disclosure, the decision-making results of whether to select AA or BB based on the objective function are displayed, making it easy to see what each objective function emphasizes.

[0063] For this reason, as mentioned above, state data related to "match with career aspirations" as shown in Figure 8 is used, and it is desirable to select state data that will likely result in different optimizations depending on the objective function as information representing a representative state. For example, "for task x to which someone needs to be assigned, Mr. AA, who has no experience but whose career aspirations match, and Mr. BB, who does not have the same career aspirations but whose experience matches," are prepared as examples of information representing a representative state. For example, if information representing such representative states is used, it is expected that the optimization results, or assignment results, will differ between an objective function that emphasizes experience and an objective function that emphasizes career aspiration match.

[0064] Specifically, for example, the solution acquiring unit 204 derives a solution by providing information representing a predetermined state for each of the multiple objective functions. This allows the solution acquiring unit 204 to acquire a solution. Alternatively, for example, the solution acquiring unit 204 may acquire, from another device, a solution derived by the other device for each of the multiple objective functions.

[0065] Furthermore, the output control unit 202 outputs the weighting coefficients for each of the multiple objective functions in a comparable manner, and also outputs the obtained solutions, allowing the user to check the influence of the weighting coefficients on the solutions while viewing the weighting coefficients and the solutions.

[0066] The solution acquisition unit 204 may also provide information representing each of a plurality of different states to each of a plurality of objective functions and acquire a solution based on the objective functions. For example, in the case of a persona, the solution acquisition unit 204 provides persona data to each of a plurality of objective functions and acquires a solution based on the objective functions. Then, the output control unit 202 outputs the acquired solution for each of the plurality of objective functions for each of the plurality of states.

[0067] Figure 9 is an explanatory diagram showing an example of a screen displaying a comparison of weighting coefficients for each objective function and the optimization results of job assignment for each persona. For example, suppose there is a decision-making (optimization problem) to assign jobs e and f to two employees, Mr. AA and Mr. BB. It is assumed that one person will be assigned to each job. Furthermore, it is assumed that job e is a high-load job, although it is desirable for the employee to have experience in the aa sales department.

[0068] In FIG. 9, weighting coefficients of feature quantities are displayed for each of objective functions ZX and ZY, which determine which job to assign to which persona (employee).

[0069] In Figure 9, persona AA and persona BB are shown as representative situations for employee-related personas. Persona AA's work history includes sales experience in the aa sales department and sales experience in the bb sales department, and her family situation is that she has been raising children since December 2020. On the other hand, persona BB's work history includes planning experience in the cc planning department and sales experience in the bb sales department, and her family situation is that she does not have childcare or elderly care responsibilities.

[0070] FIG. 9 shows the optimization results, which are solutions obtained from objective functions ZX and ZY, which determine which personas (employees) are assigned which tasks.

[0071] Furthermore, if the difference between the weighting factors for each feature is equal to or greater than a threshold, the output control unit 202 may highlight those weighting factors. The highlighting method is not particularly limited. In FIG. 9, since there is a large difference between the feature "family circumstances" between the objective function ZX and the objective function ZY, the two weighting factors are highlighted by being surrounded by a dotted frame. Furthermore, since there is a large difference between the feature "match with experience" between the objective function ZX and the objective function ZY, the two weighting factors are highlighted by being surrounded by a dotted frame.

[0072] In Figure 9, the objective function ZX has a lower weighting coefficient for the feature "family circumstances" and a higher weighting coefficient for the feature "match with experience" than the objective function ZY. Therefore, the objective function ZX places more importance on "match with experience" than the objective function ZY. On the other hand, the objective function ZY places more importance on "family circumstances" than the objective function ZX.

[0073] Since job e is a job for which experience in the aa sales department is desirable, the optimization results for objective function ZX, which emphasizes "match with experience," assign job e to persona AA and job f to persona BB.

[0074] Since job e is a high-load job, the optimization results for objective function ZY, which emphasizes "family circumstances," assign job f to persona AA and job e to persona BB.

[0075] Furthermore, although not shown, if the difference between solutions due to differences in state such as persona is equal to or greater than a specific difference, the output control unit 202 may highlight those solutions.

[0076] In FIG. 9, the output control unit 202 displays all the information on one screen, but it may also display the information on multiple switchable screens.

[0077] 10 is a flowchart showing an example of an operation of the visualization device 20 according to the explanatory example 2. The coefficient acquisition unit 201 acquires a weighting coefficient of a feature amount for each objective function (step S221). Next, the solution acquisition unit 204 acquires a solution for each state for each objective function (step S222). Then, the output control unit 202 causes the display device to display the weighting coefficient of the feature amount and the solution for each objective function (step S223).

[0078] As described above, in the second embodiment, the visualization device 20 outputs weighting coefficients arranged so that the weighting coefficients of feature quantities in a trade-off relationship among a plurality of feature quantities are adjacent to each other. This makes it easier to check which of the feature quantities in a trade-off relationship among the plurality of feature quantities in each objective function has a stronger influence.

[0079] Furthermore, when the difference between the weighting coefficients of the feature quantities in a trade-off relationship is equal to or greater than a threshold, the visualization device 20 outputs information indicating that the difference is equal to or greater than a threshold, which allows the influence of the intention in the trade-off relationship to be easily confirmed.

[0080] Furthermore, the visualization device 20 outputs information indicating that there is a trade-off relationship. This makes it easy to understand which of the multiple feature quantities is in a trade-off relationship. Furthermore, the visualization device 20 outputs the difference in weighting coefficients for each of the plurality of feature quantities in the plurality of objective functions, thereby making it possible to easily confirm the difference in the influence of each intention between different objective functions.

[0081] Furthermore, the visualization device 20 outputs, for each of a plurality of objective functions, a solution obtained based on the objective function to which information representing a predetermined state has been added, along with a weighting coefficient for each of a plurality of feature quantities, thereby making it possible to easily confirm the effect of differences in weighting coefficients on the solution.

[0082] Furthermore, the visualization device 20 may output, for each objective function, a solution obtained based on the objective function for each of a plurality of states, thereby making it possible to easily check the influence on the solution of differences in weighting coefficients in different states.

[0083] This concludes the description of each embodiment. Note that each embodiment may be used in combination. Furthermore, for example, in each embodiment, the visualization device may be configured to include each functional unit and part of the information. For example, the visualization device 20 in embodiment 2 may be configured to include a coefficient acquisition unit 201, an output control unit 202, and a relationship information acquisition unit 203. For example, the visualization device 20 in embodiment 2 may be configured to include a coefficient acquisition unit 201, an output control unit 202, and a solution acquisition unit 204.

[0084] Furthermore, the above-described embodiments are not limited to the examples described above, and various modifications are possible. Furthermore, the configuration of the visualization device in each embodiment is not particularly limited. Each functional unit described in the embodiment may be realized by a single device (visualization device), or may be realized by a plurality of different devices, such as a visualization system.

[0085] Furthermore, buttons, information display fields, input fields, etc. (not shown) may be added to each screen. Furthermore, the position, color, and size of each item, such as a button, input field, or display field, on each screen are not particularly limited. Furthermore, the background color of the screen may be changed.

[0086] For example, in each embodiment, if the display device, which is the output device, is provided by a device different from the visualization device 10, 20, the process of generating screen information to be displayed on the display device may be performed by the output control unit 102, 202, or may be performed by the device that includes the display device.

[0087] (computer) Next, an example of a hardware configuration when the visualization device described in each embodiment is realized by a computer will be described. Fig. 11 is an explanatory diagram showing an example of a hardware configuration of a computer. For example, some or all of each device can be realized using any combination of a computer 30 and a program as shown in Fig. 11.

[0088] The computer 30 includes, for example, a processor 301, a ROM (Read Only Memory) 302, a RAM (Random Access Memory) 303, a storage device 304, a communication interface 305, and an input / output interface 306. Each component is connected to each other via a bus 307. The number of each component is not particularly limited, and each component may be one or more.

[0089] The processor 301 controls the entire computer 30. Examples of the processor 301 include a central processing unit (CPU), a digital signal processor (DSP), and a graphics processing unit (GPU). There may be multiple processors 301. The computer 30 includes a storage unit such as a read-only memory (ROM) 302, a random access memory (RAM) 303, and a storage device 304. Examples of the storage device 304 include a semiconductor memory such as a flash memory, a hard disk drive (HDD), and a solid state drive (SSD). For example, the storage device 304 stores an operating system (OS) program, application programs, and programs according to the embodiments. Alternatively, the ROM 302 stores application programs and programs according to the embodiments. The RAM 303 is used as a work area for the processor 301.

[0090] The processor 301 also loads programs stored in the storage device 304, ROM 302, etc. The processor 301 then executes each process (each processing instruction) coded in the program. The processor 301 may also download various programs via the communication network NT. The processor 301 also functions as a part or all of the computer 30. The processor 301 may then execute the processes or instructions in the illustrated flowchart based on the program.

[0091] The communication interface 305 is connected to a communication network NT such as a LAN (Local Area Network) or WAN (Wide Area Network) via a wireless or wired communication line. The communication network NT may be configured with multiple communication networks. As a result, the computer 30 is connected to external devices and external computers 30 via the communication network NT. The communication interface 305 serves as an interface between the communication network NT and the inside of the computer 30. The communication interface 305 also controls the input and output of data from external devices and external computers 30.

[0092] Furthermore, the input / output interface 306 is connected to at least one of an input device, an output device, and an input / output device. The connection method may be wireless or wired. Examples of the input device include a keyboard, a mouse, and a microphone. Examples of the output device include a display device, a lighting device, and a speaker that is an audio output device that outputs audio. Examples of the input / output device include a touch panel display. The input device, output device, and input / output device may be built into the computer 30 or may be external.

[0093] The hardware configuration of the computer 30 is an example. The computer 30 may have some of the components shown in FIG. 11 . The computer 30 may have components other than those shown in FIG. 11 . For example, the computer 30 may have a drive device or the like. The processor 301 may then read programs and data stored in a recording medium attached to the drive device or the like into the RAM 303. Examples of non-transitory tangible recording media include optical disks, flexible disks, magneto-optical disks, and USB (Universal Serial Bus) memories. As described above, the computer 30 may have input devices such as a keyboard and a mouse. The computer 30 may have an output device such as a display. The computer 30 may also have an input device, an output device, and an input / output device. The computer 30 may have various sensors (not shown). The types of sensors are not particularly limited.

[0094] This concludes the description of the hardware configuration of the visualization device. Furthermore, there are various variations in the method of realizing the visualization device. For example, the visualization device may be realized by any combination of different computers and programs for each component. Furthermore, multiple components of the visualization device may be realized by any combination of a single computer and program.

[0095] Furthermore, some or all of the components of each device, such as a visualization device, may be realized by circuits for specific applications. Furthermore, some or all of each device may be realized by general-purpose circuits including a processor, such as an FPGA (Field Programmable Gate Array). Furthermore, some or all of each device may be realized by a combination of circuits for specific applications and general-purpose circuits. Furthermore, these circuits may be a single integrated circuit. Alternatively, these circuits may be divided into multiple integrated circuits. Furthermore, the multiple integrated circuits may be configured by being connected via a bus or the like.

[0096] Furthermore, when some or all of the components of each device are realized by a plurality of computers, circuits, etc., the plurality of computers, circuits, etc. may be centrally located or distributed.

[0097] The visualization method described in each embodiment is realized by a computer such as a visualization device executing the visualization method. The visualization method is also realized by a computer such as a visualization device executing a program prepared in advance. The program described in each embodiment can be stored on a hard disk drive (HDD), an SSD, a flexible disk, an optical disk, or the like. , magnetic The program is recorded on a computer-readable recording medium such as an optical disk or a USB memory. The program is then read from the recording medium and executed by a computer. The program may also be distributed via a communications network NT.

[0098] The functions of each component of each device, such as the visualization device in each embodiment described above, may be realized by hardware, such as a computer, or may be realized by a computer or firmware under program control.

[0099] Although the present disclosure has been described above with reference to various embodiments, the present disclosure is not limited to the above embodiments. The configuration and details of each of the present disclosures may include embodiments to which various modifications that would be apparent to those skilled in the art are applied within the scope of the present disclosure. The present disclosure may also include embodiments in which the details described herein are appropriately combined or substituted as necessary. For example, details described using a particular embodiment may also be applied to other embodiments to the extent that no contradiction occurs. For example, although multiple operations are described in sequence in the form of a flowchart, the order of the descriptions does not limit the order in which the multiple operations are performed. Therefore, when implementing each embodiment, the order of the multiple operations may be changed as long as it does not interfere with the content.

[0100] Some or all of the above-described embodiments can be described as follows: However, some or all of the above-described embodiments are not limited to the following.

[0101] (Appendix 1) obtaining a weighting coefficient for each of a plurality of feature quantities for each of a plurality of different objective functions; outputting the weighting coefficients obtained for each of the plurality of objective functions in a comparable manner; This includes: the weighting coefficient for each of the plurality of feature quantities represents the degree to which the feature quantity influences a solution of each of the plurality of objective functions; Visualization method. (Appendix 2) acquiring relationship information representing feature quantities that are in a trade-off relationship among the plurality of feature quantities; This includes: In the output, the weighting coefficients for each of the plurality of feature amounts are arranged and output for each of the plurality of objective functions based on the acquired relationship information such that weighting coefficients of feature amounts having the trade-off relationship among the plurality of feature amounts are adjacent to each other. Visualization method described in Appendix 1. (Appendix 3) In the output, when the difference between the weighting coefficients of the feature amounts having the trade-off relationship is equal to or greater than a threshold, information indicating that the difference is equal to or greater than the threshold is output. Visualization method described in Appendix 2. (Appendix 4) In the output, information indicating that the trade-off relationship exists is output. 4. A visualization method according to claim 2 or 3. (Appendix 5) In the output, a difference between the weighting coefficients for each of the plurality of feature quantities in the plurality of objective functions is output. 5. A visualization method according to any one of appendices 1 to 4. (Appendix 6) For each of the plurality of objective functions, a solution obtained based on the objective function given information representing a predetermined state is obtained. This includes: In the output, the weighting coefficients are output in a comparable manner for each of the plurality of objective functions, and the obtained solution is also output. Visualization method described in Appendix 1. (Appendix 7) In the acquisition of the solution, for each of a plurality of states, a solution obtained based on the objective function to which information representing the state is given is acquired for each of the plurality of objective functions; In the output, for each of the plurality of states, output the obtained solution for each of the plurality of objective functions. Visualization method described in Appendix 6. (Appendix 8) Each of the plurality of objective functions is an objective function generated by inverse reinforcement learning. 8. A visualization method according to any one of appendices 1 to 7. (Appendix 9) a coefficient acquisition means for acquiring a weighting coefficient for each of a plurality of feature quantities for each of a plurality of different objective functions; an output control means for outputting the weighting coefficients obtained for each of the plurality of objective functions in a comparable manner; Equipped with the weighting coefficient for each of the plurality of feature quantities represents the degree to which the feature quantity influences a solution of each of the plurality of objective functions; Visualization device. (Appendix 10) a relationship information acquisition means for acquiring relationship information representing feature quantities in a trade-off relationship among the plurality of feature quantities; Equipped with the output control means outputs, for each of the plurality of objective functions based on the acquired relationship information, the weighting coefficients for the plurality of feature quantities such that the weighting coefficients of the feature quantities in the trade-off relationship among the plurality of feature quantities are adjacent to each other. 10. The visualization device of claim 9. (Appendix 11) a solution acquisition means for acquiring a solution obtained based on each of the plurality of objective functions to which information representing a predetermined state is given; Equipped with the output control means outputs the weighting coefficients for each of the plurality of objective functions in a comparable manner, and outputs the obtained solution. 10. The visualization device of claim 9. (Appendix 12) Each of the plurality of objective functions is an objective function generated by inverse reinforcement learning. 12. A visualization device according to any one of appendices 9 to 11. (Appendix 13) On the computer, obtaining a weighting coefficient for each of a plurality of feature quantities for each of a plurality of different objective functions; outputting the weighting coefficients obtained for each of the plurality of objective functions in a comparable manner; Execute the process, the weighting coefficient for each of the plurality of feature quantities represents the degree to which the feature quantity influences a solution of each of the plurality of objective functions; A non-transitory recording medium readable by the computer that records a program for executing processing. (Appendix 14) Each of the plurality of objective functions is an objective function generated by inverse reinforcement learning. 14. A recording medium according to claim 13. (Appendix 15) The computer, acquiring relationship information representing feature quantities that are in a trade-off relationship among the plurality of feature quantities; Execute the process, In the outputting process, the weighting coefficients for the plurality of feature quantities are output in an arranged manner, for each of the plurality of objective functions, based on the acquired relationship information, such that weighting coefficients of feature quantities having the trade-off relationship among the plurality of feature quantities are adjacent to each other. 15. A recording medium according to claim 13 or 14. (Appendix 16) The computer, For each of the plurality of objective functions, a solution obtained based on the objective function given information representing a predetermined state is obtained. Execute the process, In the outputting process, the weighting coefficients are output in a comparable manner for each of the plurality of objective functions, and the obtained solution is also output. 15. A recording medium according to claim 13 or 14. (Appendix 17) On the computer, obtaining a weighting coefficient for each of a plurality of feature quantities for each of a plurality of different objective functions; outputting the weighting coefficients obtained for each of the plurality of objective functions in a comparable manner; Execute the process, the weighting coefficient for each of the plurality of feature quantities represents the degree to which the feature quantity influences a solution of each of the plurality of objective functions; A program that executes a process. (Appendix 18) Each of the plurality of objective functions is an objective function generated by inverse reinforcement learning. 17. The program described in Appendix 17. [Explanation of symbols]

[0102] 10,20 Visualization device 30 Computer 101,201 Coefficient acquisition section 102,202 Output control section 203 Related Information Acquisition Unit 204 Solution acquisition part 301 processor 302 ROM 303 RAM 304 Storage device 305 Communication Interface 306 Input / Output Interface 307 Bus NT Communications Network A, C, D, F features AA,BB Persona X,Y,ZX,ZY Objective functions

Claims

1. obtaining a weighting coefficient for each of a plurality of feature quantities for each of a plurality of different objective functions; acquiring relationship information representing feature quantities in a trade-off relationship among the plurality of feature quantities; outputting the weighting coefficients obtained for each of the plurality of objective functions in a comparable manner; This includes: the weighting coefficient for each of the plurality of feature quantities represents a degree of influence of the feature quantity on a solution of each of the plurality of objective functions; In the output, the weighting coefficients for each of the plurality of feature amounts are arranged and output for each of the plurality of objective functions based on the acquired relationship information such that weighting coefficients of feature amounts having the trade-off relationship among the plurality of feature amounts are adjacent to each other. Visualization method.

2. In the output, when the difference between the weighting coefficients of the feature amounts having the trade-off relationship is equal to or greater than a threshold, information indicating that the difference is equal to or greater than the threshold is output. The visualization method according to claim 1 .

3. In the output, information indicating that the trade-off relationship exists is output. The visualization method according to claim 1 or 2.

4. In the output, a difference between the weighting coefficients for each of the plurality of feature quantities in the plurality of objective functions is output. The visualization method according to any one of claims 1 to 3.

5. For each of the plurality of objective functions, a solution obtained based on the objective function given information representing a predetermined state is obtained. This includes: In the output, the weighting coefficients are output in a comparable manner for each of the plurality of objective functions, and the obtained solution is also output. The visualization method according to claim 1 .

6. In the acquisition of the solution, for each of a plurality of states, a solution obtained based on the objective function to which information representing the state is given is acquired for each of the plurality of objective functions; In the output, for each of the plurality of states, output the obtained solution for each of the plurality of objective functions. The visualization method according to claim 5 .

7. Each of the plurality of objective functions is an objective function generated by inverse reinforcement learning. The visualization method according to any one of claims 1 to 6.

8. a coefficient acquisition means for acquiring a weighting coefficient for each of a plurality of feature quantities for each of a plurality of different objective functions; a relationship information acquisition means for acquiring relationship information representing feature quantities in a trade-off relationship among the plurality of feature quantities; an output control means for outputting the weighting coefficients obtained for each of the plurality of objective functions in a comparable manner; Equipped with the weighting coefficient for each of the plurality of feature quantities represents a degree of influence of the feature quantity on a solution of each of the plurality of objective functions; the output control means outputs, for each of the plurality of objective functions, the weighting coefficients for the plurality of feature quantities such that the weighting coefficients of the feature quantities in the trade-off relationship among the plurality of feature quantities are adjacent to each other, based on the acquired relationship information. Visualization device.

9. On the computer, obtaining a weighting coefficient for each of a plurality of feature quantities for each of a plurality of different objective functions; acquiring relationship information representing feature quantities in a trade-off relationship among the plurality of feature quantities; outputting the weighting coefficients obtained for each of the plurality of objective functions in a comparable manner; Execute the process, the weighting coefficient for each of the plurality of feature quantities represents a degree of influence of the feature quantity on a solution of each of the plurality of objective functions; In the output, the weighting coefficients for each of the plurality of feature amounts are arranged and output for each of the plurality of objective functions based on the acquired relationship information such that weighting coefficients of feature amounts having the trade-off relationship among the plurality of feature amounts are adjacent to each other. program.

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