Visualization method, visualization device, and program

Abstract

This visualization device comprises an acquisition unit and an output control unit. The acquisition unit acquires, for each of a plurality of different solutions to an optimization problem which are obtained on the basis of an objective function, feature amount values of the objective function for each solution. The output control unit outputs, in a comparable manner, the feature amount values acquired for the optimal solution among the plurality of solutions and the feature amount values acquired for executable solutions among the plurality of solutions.

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] For example, when graphing and displaying the results of multiple solutions obtained based on an objective function, there is a technology that reflects the importance or constraints of each evaluation item used in calculating the solution on the axis or the background of the axis (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] International Publication No. 2013 / 179577 [Patent Document 2] International Publication No. 2021 / 130916 Summary of the Invention [Problem to be solved by the invention]

[0006] When an optimal solution is obtained by the objective function, there is a problem in that it is difficult to know whether the optimal solution is appropriate for use.

[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 each solution obtained by an objective function. [Means for solving the problem]

[0008] A visualization method according to one aspect of the present disclosure obtains, for each of a plurality of different solutions to an optimization problem obtained based on an objective function, values ​​of feature quantities of the objective function for the respective solutions, and outputs the value of the feature quantities obtained for an optimal solution among the plurality of solutions and the value of the feature quantities obtained for a feasible solution among the plurality of solutions in a comparable manner.

[0009] A visualization device according to one aspect of the present disclosure includes an acquisition means for acquiring, for each of a plurality of different solutions to an optimization problem obtained based on an objective function, values ​​of features of the objective function in the respective solutions, and an output control means for outputting, in a comparable manner, the values ​​of the features acquired for an optimal solution among the plurality of solutions and the values ​​of the features acquired for a feasible solution among the plurality of solutions.

[0010] A program in one aspect of the present disclosure causes a computer to acquire, for each of a plurality of different solutions to an optimization problem obtained based on an objective function, index values ​​of feature quantities of the objective function in the respective solutions, and output the value of the feature quantity acquired for an optimal solution among the plurality of solutions and the value of the feature quantity acquired for a feasible solution among the plurality of solutions in a comparable manner.

[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 each solution obtained by 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 plurality of solutions. [Figure 5] FIG. 10 is an explanatory diagram showing an example of a screen displaying comparative index values. [Figure 6] FIG. 10 is an explanatory diagram showing an example of a screen for selecting a feature amount of interest. [Figure 7] FIG. 10 is an explanatory diagram showing an example of a screen on which a graph is displayed. [Figure 8] 10 is a flowchart illustrating an example of an operation of the visualization device according to the second embodiment. [Figure 9] 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 target may be an expert or the like. There may be multiple targets. Constraints are items that must be observed when making a decision. Features, i.e., viewpoints, are items that are taken into consideration when making a decision.

[0018] Furthermore, solutions obtained based on an objective function can be classified into optimal solutions and feasible solutions. An optimal solution is a solution that minimizes or maximizes the objective function. On the other hand, a feasible solution is a solution (admissible solution) that does not minimize or maximize the objective function but satisfies the specified constraints.

[0019] (Embodiment 1) First, a description will be given of basic functions of a visualization device in the first embodiment. Fig. 1 is a block diagram showing an example of the configuration of a visualization device according to the first embodiment.

[0020] For example, a user may optimize their behavior using a learned objective function. However, the optimal solution that minimizes or maximizes the optimal solution may not necessarily be suitable for each usage scenario. For this reason, it is desirable for the user to select a solution suitable for each usage scenario by referring to feasible solutions other than the optimal solution. Therefore, the visualization device 10 visualizes feature quantities affected by the solution for the optimal solution and feasible solution obtained by the objective function. Note that feature quantities may also be called indices. In FIG. 1, the visualization device 10 includes an acquisition unit 101 and an output control unit 102.

[0021] The acquisition unit 101 acquires values ​​of the feature quantities of the objective function for each of a plurality of different solutions to the optimization problem obtained based on the row objective function. The objective function is an objective function that reflects the intention of a subject, such as an expert. For example, the objective function is a criterion for deriving an optimal solution for a certain action. For example, weighting of the feature quantities of the objective function may be set based on experience or the like, or may be obtained by learning based on a decision-making history. This learning is, for example, inverse reinforcement learning. The objective function may also be an objective function obtained by multi-objective optimization.

[0022] Furthermore, the term "behavior" here includes, for example, work. In the following description, the term "work" may be used in the description. For example, the behavior may be, without particular limitation, work to determine the order of work, scheduling work such as allocating shifts, matching work such as task assignment, or work to determine a combination of some kind, such as resource allocation to determine a combination of dishes within a calorie limit. For example, as described above, the multiple solutions include an optimal solution and a feasible solution. There is no particular limitation on the number of feasible solutions.

[0023] Here, the value of a feature may be referred to as a feature index or index value. Index values ​​are explained using an objective function for determining work schedules as an example. Feature values ​​for the objective function include, for example, labor costs, number of employees, vacation requests, skill diversity, and compatibility between members. For example, if the feature is labor costs, the index value is the labor cost amount in the solution. The labor cost amount in the optimal solution may differ from the labor cost amount in the feasible solution. The labor cost amount in the optimal solution may also be higher than the labor cost amount in the feasible solution. Thus, when multiple feature values ​​are considered as a whole, the optimal solution is one that minimizes or maximizes the objective function. However, when focusing on individual feature values, the optimal solution is not necessarily superior to other solutions.

[0024] The representation format of the index value is not particularly limited. The index value is expressed in different units depending on the feature. For example, if the feature value is labor costs, the unit of the index value is monetary, but if the feature value is average working hours, the unit of the index value is hours.

[0025] Specifically, for example, the acquiring unit 101 may acquire the index value by calculating the index value using the objective function and the solution. Alternatively, a device different from the visualization device 10 may calculate the index value, and the acquiring unit 101 may acquire the index value from that device. The timing at which the acquiring unit 101 acquires the index value is not particularly limited. For example, the acquiring unit 101 may acquire the index value when an instruction to compare index values ​​is received from a user.

[0026] The output control unit 102 outputs an index value obtained for an optimal solution among the multiple solutions and an index value obtained for a feasible solution among the multiple solutions in a manner that allows comparison. The output format by the output control unit 102 is, for example, output to an output device such as a display device or an audio output device, and is not particularly limited. For example, the output device may be provided in the visualization device 10, or may be provided in another device connected to the visualization device 10 via a communication network or the like.

[0027] Specifically, for example, the output control unit 102 may sort the index values ​​acquired for the optimal solution and the index values ​​acquired for the feasible solution in a predetermined order and output them. For example, the predetermined order may be, for example, from highest index value to lowest index value. This makes it easier for the user to select a solution with a better index value for the feature. Furthermore, when sorting and outputting the index values, the output control unit 102 may output each ranking together.

[0028] 2 is a flowchart showing an example of an operation of the visualization device 10 according to the first embodiment. The acquisition unit 101 acquires index values ​​of feature quantities that explain the objective function when solving the objective function (step S101). In step S101, the acquisition unit 101 may acquire the index values ​​by calculating them. Alternatively, in step S101, a device different from the visualization device 10 may calculate the index values, and the acquisition unit 101 may acquire the index values ​​from that device.

[0029] The output control unit 102 outputs the index value for the optimal solution and the index value for the feasible solution in a comparable manner (step S102). In step S102, the output control unit 102 may sort the index value acquired for the optimal solution and the index value acquired for the feasible solution in descending order and output them.

[0030] As described above, in the first embodiment, the visualization device 10 outputs an index value for an optimal solution obtained based on an objective function and an index value for a feasible solution in a manner that allows comparison between them. This makes it easier to check each solution obtained by the objective function.

[0031] The first embodiment is not limited to the above-described examples and can be modified in various ways. 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 as a system.

[0032] (Embodiment 2) Next, a second embodiment will be described in detail with reference to the drawings. In the second embodiment, specific examples such as a screen example that compares and outputs an index value for a feasible solution and an index value for an optimal solution will be described. Below, explanations of content that overlaps with the above explanation will be omitted to the extent that the explanation of the second embodiment is not unclear.

[0033] 3 is a block diagram showing an example of the configuration of a visualization device according to the second embodiment. The visualization device 20 includes an acquisition unit 201, an output control unit 202, a feature amount receiving unit 203, a comparison solution receiving unit 204, and a utilization solution receiving unit 205. In the second embodiment, the feature amount receiving unit 203, the comparison solution receiving unit 204, and the utilization solution receiving unit 205 are newly added to the components of the first embodiment. The acquisition unit 201 and the output control unit 202 have the basic functions of the acquisition unit 101 and the output control unit 102 described in the first embodiment, respectively.

[0034] FIG. 4 is an explanatory diagram showing an example of multiple solutions. Take the objective function of the task of determining work schedules as an example. Therefore, in FIG. 4, each solution is a work schedule for each employee. As shown in FIG. 4, the work schedule for each employee differs. Note that while FIG. 4 shows an optimal solution, a feasible solution A, and a feasible solution B, the number of feasible solutions is not particularly limited.

[0035] (Focus on one feature) Returning to the explanation of FIG. 3, first, an example will be explained in which one feature amount is designated from among a plurality of feature amounts.

[0036] As described in the first embodiment, the acquisition unit 201 acquires an index value of a feature for each of a plurality of different solutions obtained based on an objective function in which behavioral factors are used as features. The feature here may be a feature specified by a user from the plurality of feature values. An example of accepting a feature value designation will be described. The feature value accepting unit 203 may accept a selection of one of the plurality of feature values ​​through a user operation. The device operated by the user may be an input device of the visualization device 20, or may be a user terminal device connected to the visualization device 20 via a communication network or the like, and is not particularly limited. In the following description, when other information is accepted, the device operated by the user is also not particularly limited.

[0037] Next, as described in the first embodiment, the output control unit 202 outputs the index value obtained for the optimal solution and the index value obtained for the feasible solution in a manner that allows them to be compared. The output format is not particularly limited. For example, the output device may be provided in the visualization device 20, or may be provided in another device connected to the visualization device 20 via a communication network or the like.

[0038] Specifically, for example, as described in embodiment 1, the output control unit 202 may sort and output the index values ​​obtained for the optimal solution and the index values ​​obtained for the feasible solution in a predetermined order.

[0039] Alternatively, for example, the output control unit 202 may output the difference between the index value acquired for the optimal solution and the index value acquired for the feasible solution. For example, the difference may be a value obtained by subtracting the index value acquired for the feasible solution from the index value acquired for the optimal solution, or a value obtained by subtracting the index value acquired for the optimal solution from the index value acquired for the feasible solution, and is not particularly limited.

[0040] FIG. 5 is an explanatory diagram showing an example of a screen for comparatively displaying index values. In FIG. 5, the feature value is labor cost as an example, and the index value is the labor cost amount. In FIG. 5, the output control unit 202 displays each amount on the display device. In FIG. 5, the output control unit 202 displays the labor costs for the optimal solution and the labor costs for the feasible solutions in ascending order of labor cost. Furthermore, the output control unit 202 also outputs the ranking of the amounts. Furthermore, for feasible solutions, the output control unit 202 outputs the difference between the labor costs for the optimal solution and the labor costs for the feasible solution. In FIG. 5, the difference is the value obtained by subtracting the labor cost of the optimal solution from the labor cost of feasible solution B.

[0041] FIG. 5, for example, displays the labor costs for the optimal solution, feasible solution A, and feasible solution B. According to FIG. 5, the labor cost for feasible solution B is 880,000 yen, the lowest. The labor cost for the optimal solution is 89,000 yen, the second lowest. The labor cost for feasible solution A is 910,000 yen, the third lowest. Also, according to FIG. 5, the difference in labor cost between feasible solution B and the optimal solution is -10,000 yen. Also, according to FIG. 5, the difference in labor cost between feasible solution B and the optimal solution is +20,000 yen.

[0042] In this way, when focusing on labor costs, feasible solution B may be better than the optimal solution. For example, even if the user normally selects the optimal solution, if the user wants to prioritize labor costs, the user may select feasible solution B.

[0043] Returning to the explanation of FIG. 3, the output control unit 202 selects a predetermined number of feasible solutions from among a plurality of feasible solutions, for example, based on a predetermined order of index values. The output control unit 202 outputs the index values ​​for the selected predetermined number of feasible solutions and the index value for the optimal solution so that they can be compared. The predetermined order of index values ​​may be from highest to lowest index value or from lowest to highest index value. The predetermined number may be a fixed value determined in advance, or may be a number newly specified by the user.

[0044] The output control unit 202 may also output a graph of the index values ​​for the optimal solution and the index values ​​for the feasible solution. The type of graph is not particularly limited and may be a bar graph, a pie chart, a band graph, or the like.

[0045] (multiple features) Although the example described above uses a case where one feature is specified by the user, the present invention is not limited to this example. Multiple features that explain the objective function may be specified by the user, and index values ​​may be obtained for each of the multiple feature values.

[0046] The acquiring unit 201 acquires, for each of the plurality of solutions, an index value for each of the plurality of feature quantities in the objective function.

[0047] As in the example described with respect to one feature, an index value may be acquired for each of the specified multiple feature amounts. Specifically, for example, the feature amount receiving unit 203 may receive a designation of some of the multiple feature amounts through a user operation. Note that, as described above, the device operated by the user is not particularly limited. Then, for example, the acquiring unit 201 acquires an index value for each of the multiple feature amounts designated by the user.

[0048] Next, when the index values ​​for each of the plurality of feature quantities are obtained, the output control unit 202 outputs the obtained index values ​​for each of the plurality of solutions so that the index values ​​for predetermined feature quantities among the plurality of feature quantities are arranged in a predetermined order. The predetermined feature quantities may be fixed feature quantities or feature quantities specified by the user.

[0049] An example in which a feature is designated by a user will be described. For example, the feature receiving unit 203 may receive a selection of one of a plurality of feature through a user operation. Note that, as described above, the device to be operated by the user is not particularly limited.

[0050] Fig. 6 is an explanatory diagram showing an example of a screen for selecting a feature of interest. In Fig. 6, for example, the output control unit 202 causes the display device to display a screen on which the feature of interest most among multiple feature amounts can be selected. In Fig. 6, buttons for selecting feature amounts are displayed for each feature amount. In Fig. 6, the feature amount "labor cost" has been selected. For example, when the "Confirm" button is tapped, the feature amount receiving unit 203 receives the selection of the feature amount "labor cost."

[0051] Then, the output control unit 202 outputs the index values ​​acquired for each feature for each of the multiple solutions so that the index values ​​for the feature "labor costs" are arranged in a predetermined order (for example, from lowest to highest).

[0052] Returning to the explanation of FIG. 3, if there are multiple feasible solutions and all of them are output, it would be difficult for the user to compare the index values ​​and select a solution. Therefore, the output control unit 202 may output index values ​​for a predetermined number of feasible solutions selected from the multiple feasible solutions based on a predetermined order of the index values, as well as an index value for the optimal solution. This allows the user to narrow down the multiple feasible solutions to a portion of the feasible solutions and compare their index values.

[0053] Furthermore, the output control unit 202 graphs the index values ​​obtained for the optimal solution among the multiple solutions and the index values ​​obtained for the feasible solutions among the multiple solutions, and outputs the graphs. The type of graph is not particularly limited and may be a line graph, a bar graph, a pie chart, a band graph, or the like.

[0054] Fig. 7 is an explanatory diagram showing an example of a screen on which a graph is displayed. In Fig. 7, the output control unit 202 displays a line graph of index values ​​of the solution. In the line graph of Fig. 7, the horizontal axis represents the feature amount, and the vertical axis represents the index value of the feature amount. The vertical axis has different units for each feature amount. Note that the index values ​​may be normalized so that the units are the same when graphing.

[0055] In the example of Fig. 7, a line graph is displayed showing the index values ​​of each feature amount for each of the optimal solution and the feasible solution A. In this way, the index values ​​for some of the feasible solutions and the index value for the optimal solution may be displayed. The some of the feasible solutions may be specified by the user, or may be a predetermined number (e.g., 1) of the feasible solutions in order of best among the multiple solutions.

[0056] The order of arrangement of feature quantities in the line graph is not particularly limited. For example, a specified feature quantity may be displayed at the top. As in the above example, the designation method may be such that the feature quantity designation is received by the feature quantity receiving unit 203. Alternatively, although not shown, the feature quantities may be arranged in descending order of the difference between the index value for the optimal solution and the index value for the feasible solution. If the units of the index values ​​are different, normalized differences may be compared.

[0057] Furthermore, although not shown, the output control unit 202 may highlight a feature having a large difference between the index value for the optimal solution and the index value for the feasible solution. Specifically, for example, the output control unit 202 may highlight the index value of the feature or the name of the feature.

[0058] This concludes the explanation of the case where there are multiple feature amounts.

[0059] (multiple objective functions) Although the above explanation has been given using an example where there is one objective function, there are cases where multiple different objective functions are derived. Multiple objective functions are created for the same behavior based on different decision-making histories. For example, multiple objective functions are created for different subjects based on their decision-making histories, or for the same subject based on their decision-making histories at different times, such as different time periods.

[0060] The acquiring unit 201 acquires an index value of the feature amount for each of a plurality of solutions derived for each of a plurality of different objective functions. Note that, as described above, an index value may be acquired for each of the plurality of feature amounts.

[0061] The output control unit 202 then outputs an index value for the optimal solution and an index value for the feasible solution for each of the different objective functions in a comparable manner. For example, when creating a graph, the output control unit 202 may output a graph for each objective function. Alternatively, the output control unit 202 may output graphs for the same index value for each of the different objective functions in a superimposed manner.

[0062] (Specify feasible solutions to compare) For example, when there are multiple feasible solutions, the index value for the feasible solution specified by the user may be compared with the index value for the optimal solution. For example, the output control unit 202 may display the feasible solutions. Then, the comparison solution receiving unit 204 receives, through a user operation, a designation of a feasible solution to be compared from the multiple feasible solutions. As described above, the device to be operated by the user is not particularly limited. The number of feasible solutions designated here is not particularly limited. Then, the acquisition unit 201 may acquire index values ​​for the objective function for the designated feasible solution and the optimal solution.

[0063] (Specify the solution to use) Furthermore, the user selects a solution to be used based on a comparison of the index values. The solution-to-be-used accepting unit 205 accepts, through a user operation, a designation of a solution to be used from among the multiple solutions. For example, the output control unit 202 may output the designated solution.

[0064] Fig. 8 is a flowchart showing an example of an operation of the visualization device 20 according to the second embodiment. In the example of operation in Fig. 8, there is one objective function, a predetermined number of feasible solutions are selected in order of index values ​​of feature quantities specified by a user, and the index values ​​of the selected predetermined number of feasible solutions and the optimal solution are compared and output.

[0065] The acquiring unit 201 acquires an index value for each feature for each solution of the objective function (step S201). Next, the feature receiving unit 203 receives a designation of the feature (step S202). Note that the order of the processing of steps S201 and S202 is not limited to the order shown in FIG. 8, and step S201 may be executed after step S202.

[0066] The output control unit 202 selects a predetermined number of feasible solutions based on a predetermined order of index values ​​of the specified feature quantities (step S203). The predetermined order of index values ​​is either high to low or low to high. Then, the output control unit 202 graphs the index values ​​of the selected feasible solutions and the index values ​​of the optimal solution, and displays the graph on the display device (step S204).

[0067] As described above, in the second embodiment, the visualization device 20 outputs index values ​​for a predetermined number of feasible solutions selected from a plurality of solutions based on a predetermined order of index values, and the index value for the optimal solution, so that the index values ​​can be compared. This allows the user to narrow down the feasible solutions to those with better features that the user considers important, and compare them with the optimal solution. This improves the ease of checking each solution obtained by the objective function.

[0068] Furthermore, the visualization device 20 outputs the index values ​​of each solution in a predetermined order. For example, the visualization device 20 outputs the index values ​​in a predetermined order. This allows the user to easily check solutions with good index values ​​for features that the user considers important. Therefore, it is possible to improve the ease of checking each solution obtained by the objective function.

[0069] Furthermore, the visualization device 20 graphs and outputs the index values ​​obtained for the optimal solution and the index values ​​obtained for the feasible solutions. This allows for comparison in an easy-to-read format. Therefore, it is possible to improve the ease of checking each solution obtained by the objective function. Note that the visualization device 20 may obtain an index value for each of multiple feature quantities in the objective function for each of multiple solutions, and output a graph of the index values ​​obtained for the optimal solution and the index values ​​obtained for the feasible solutions. This allows for comparison in an easy-to-read format of the index values ​​for each of the various feature quantities in the optimal solution and the feasible solutions. Therefore, it is possible to improve the ease of checking each solution obtained by the objective function.

[0070] The feasible solution may be a specified feasible solution from among multiple solutions, allowing the user to compare the index value for the feasible solution they want to check with the index value for the optimal solution.

[0071] The visualization device 20 also outputs the difference between the index value for the optimal solution and the index value for the feasible solution, thereby making it possible to visualize the degree of quality of the index value for each feasible solution relative to the index value for the optimal solution.

[0072] Furthermore, the visualization device 20 acquires an index value for each of the plurality of feature quantities in the objective function for each of the plurality of solutions, and outputs the acquired index values ​​for each of the plurality of solutions so that the index values ​​for a predetermined feature quantity among the plurality of feature quantities are arranged in a predetermined order. This allows the index values ​​for various feature quantities to be compared between the optimal solution and the feasible solution. This improves the ease of checking each solution obtained by the objective function.

[0073] This concludes the description of each embodiment. Note that each embodiment may be used in combination. Also, for example, in each embodiment, the visualization device may be configured to include each functional unit and part of the information.

[0074] In each embodiment, a work schedule is used as an example, but the work is not particularly limited as described above. For example, in the case of the work of determining a delivery route, the feature amount may be the time required for delivery, an index of customers such as the potential of customers included in the delivery route, the suitability of the driver who will deliver the goods, etc.

[0075] 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 multiple different devices.

[0076] Furthermore, buttons, information display fields, input fields, etc. (not shown) may be added to each screen. The position, color, and size of each item, such as a button, input field, or display field, on each screen are not particularly limited. The background color of the screen may also be changed.

[0077] For example, in each embodiment, if the display device, which is the output device, is provided by a device different from the visualization device, 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.

[0078] (computer) Next, an example of a hardware configuration in which the visualization device described in each embodiment is realized by a computer will be described. Fig. 9 is an explanatory diagram showing an example of a hardware configuration of a computer. For example, some or all of the devices can be realized using any combination of a computer 30 and a program as shown in Fig. 9.

[0079] The computer 30 includes, for example, a processor 301, a read-only memory (ROM) 302, a random access memory (RAM) 303, a storage device 304, a communication interface 305, and an input / output interface 306. Each component is connected to the other via a bus 307.

[0080] 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.

[0081] 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.

[0082] The communication interface 305 is connected to a communication network NT, such as a LAN (Local Area Network) or a WAN (Wide Area Network), via a wireless or wired communication line. The communication network NT may be configured with multiple communication networks. This allows the computer 30 to be connected to external devices and external computers 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 and to external devices and external computers.

[0083] 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.

[0084] The hardware configuration of the computer 30 is an example. The computer 30 may have some of the components shown in FIG. 9. The computer 30 may have components other than those shown in FIG. 9. 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] (Appendix 1) For each of a plurality of different solutions to the optimization problem obtained based on the objective function, a value of a feature quantity of the objective function in the respective solutions is obtained; outputting the value of the feature quantity obtained for the optimal solution among the plurality of solutions and the value of the feature quantity obtained for the feasible solution among the plurality of solutions in a manner that allows them to be compared; Visualization method. (Appendix 2) In the output, the feature values ​​of a predetermined number of the feasible solutions selected from the plurality of solutions based on a predetermined order of the feature values ​​are output in a manner that allows comparison with the feature value of the optimal solution. Visualization method described in Appendix 1. (Appendix 3) In the output, the feature values ​​are output in a predetermined order. 3. A visualization method according to claim 1 or 2. (Appendix 4) In the output, the value of the feature quantity obtained for the optimal solution and the value of the feature quantity obtained for the feasible solution are graphed and output. 4. A visualization method according to any one of appendices 1 to 3. (Appendix 5) the feasible solution is a specified feasible solution among the plurality of solutions; 5. A visualization method according to any one of appendices 1 to 4. (Appendix 6) In the output, a difference between the value of the feature quantity for the optimal solution and the value of the feature quantity for the feasible solution is output. 6. A visualization method according to any one of appendices 1 to 5. (Appendix 7) In the obtaining, a value of a feature amount for each of a plurality of feature amounts in the objective function is obtained for each of the plurality of solutions; In the output, the values ​​of the feature quantities obtained for each of the plurality of solutions are output so that the values ​​of the feature quantities for a predetermined feature quantity among the plurality of feature quantities are arranged in a predetermined order. 10. The visualization method of any of appendix 1 or 6. (Appendix 8) The objective function is an objective function generated by inverse reinforcement learning. 8. A visualization method according to any one of appendices 1 to 7. (Appendix 9) an acquisition means for acquiring a value of a feature quantity of the objective function for each of a plurality of different solutions to an optimization problem obtained based on the objective function; an output control means for outputting a value of the feature quantity obtained for an optimal solution among the plurality of solutions and a value of the feature quantity obtained for a feasible solution among the plurality of solutions in a manner that allows comparison; A visualization device comprising: (Appendix 10) The objective function is an objective function generated by inverse reinforcement learning. 10. The visualization device of claim 9. (Appendix 11) On the computer, For each of a plurality of different solutions to the optimization problem obtained based on the objective function, a value of a feature quantity of the objective function in the respective solutions is obtained; outputting the value of the feature quantity obtained for the optimal solution among the plurality of solutions and the value of the feature quantity obtained for the feasible solution among the plurality of solutions in a manner that allows them to be compared; A non-transitory recording medium readable by the computer that records a program for executing processing. (Appendix 12) The objective function is an objective function generated by inverse reinforcement learning. 12. A recording medium according to claim 11. (Appendix 13) On the computer, For each of a plurality of different solutions to the optimization problem obtained based on the objective function, a value of a feature quantity of the objective function in the respective solutions is obtained; outputting the value of the feature quantity obtained for the optimal solution among the plurality of solutions and the value of the feature quantity obtained for the feasible solution among the plurality of solutions in a manner that allows them to be compared; A program that executes a process. (Appendix 14) The objective function is an objective function generated by inverse reinforcement learning. 13. The program described in Appendix 13. [Explanation of symbols]

[0093] 10,20 Visualization device 30 Computer 101,201 Acquisition Department 102,202 Output control section 203 Feature Reception Unit 204 Comparison Solution Reception Section 205 User Solution Reception Department 301 processor 302 ROM 303 RAM 304 Storage device 305 Communication Interface 306 Input / Output Interface 307 Bus A feasible solution B. Feasible solution NT Communications Network

Claims

1. For each of a plurality of different solutions to the optimization problem obtained based on the objective function, a value of a feature quantity of the objective function in the respective solutions is obtained; outputting the value of the feature quantity obtained for the optimal solution among the plurality of solutions and the value of the feature quantity obtained for the feasible solution among the plurality of solutions in a manner that allows them to be compared; Visualization method.

2. In the output, the feature values ​​of a predetermined number of the feasible solutions selected from the plurality of solutions based on a predetermined order of the feature values ​​are output in a manner that allows comparison with the feature value of the optimal solution. The visualization method according to claim 1 .

3. In the output, the feature values ​​are output in a predetermined order. The visualization method according to claim 1 or 2.

4. In the output, the value of the feature quantity obtained for the optimal solution and the value of the feature quantity obtained for the feasible solution are graphed and output. The visualization method according to any one of claims 1 to 3.

5. the feasible solution is a specified feasible solution among the plurality of solutions; The visualization method according to any one of claims 1 to 4.

6. In the output, a difference between the value of the feature quantity for the optimal solution and the value of the feature quantity for the feasible solution is output. The visualization method according to any one of claims 1 to 5.

7. In the obtaining, a value of a feature amount for each of a plurality of feature amounts in the objective function is obtained for each of the plurality of solutions; and outputting the values ​​of the feature quantities obtained for each of the plurality of solutions such that the values ​​of a predetermined feature quantity among the plurality of feature quantities are in a predetermined order. The visualization method according to any one of claims 1 to 6.

8. The objective function is an objective function generated by inverse reinforcement learning. The visualization method according to any one of claims 1 to 7.

9. an acquisition means for acquiring a value of a feature quantity of the objective function for each of a plurality of different solutions to an optimization problem obtained based on the objective function; an output control means for outputting a value of the feature quantity obtained for an optimal solution among the plurality of solutions and a value of the feature quantity obtained for a feasible solution among the plurality of solutions in a manner that allows comparison; A visualization device comprising:

10. On the computer, For each of a plurality of different solutions to the optimization problem obtained based on the objective function, a value of a feature quantity of the objective function in the respective solutions is obtained; outputting the value of the feature quantity obtained for the optimal solution among the plurality of solutions and the value of the feature quantity obtained for the feasible solution among the plurality of solutions in a manner that allows them to be compared; A program that executes a process.

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