Information processing device, program, and information processing method

Abstract

To improve the quality of the behavior of objects that users experience in virtual space. [Solution] An information processing device 2 comprising: a physical behavior acquisition unit 100 that acquires physical behavior data relating to the behavior of a physical object in a physical space; a virtual behavior acquisition unit 102 that acquires virtual behavior data relating to the behavior of a virtual object corresponding to a physical object in a virtual space, which is generated based on a set of parameters relating to a virtual space; and a determination unit 104 that determines a set of target parameters relating to calculating the behavior of a virtual object in a virtual space based on the relationship between the physical behavior data and the virtual behavior data.

Description

[[Technical Field]]

[0001] The present disclosure relates to an information processing apparatus, a program, and an information processing method. [[Background Art]]

[0002] Conventionally, a technique for allowing a user to experience phenomena in physical space in virtual space is known (for example, Patent Document 1). [[Prior Art Literature]] [[Patent Literature]]

[0003] [[Patent Document 1]] Japanese Unexamined Patent Publication No. 2000-099767 [[Summary of the Invention]] [[Problem to be Solved by the Invention]]

[0004] However, the technique described in Patent Document 1 cannot sufficiently improve the quality of the behavior of an object experienced by a user in virtual space.

[0005] The present disclosure provides a technique for improving the quality of the behavior of an object experienced by a user in virtual space. [[Means for Solving the Problem]]

[0006] An information processing apparatus according to an aspect of the present disclosure includes: a physical behavior acquisition unit that acquires physical behavior data related to the behavior of a physical object in physical space; a virtual behavior acquisition unit that acquires virtual behavior data related to the behavior of a virtual object corresponding to the physical object in the virtual space, the virtual behavior data being generated based on a parameter group related to the virtual space; and a determination unit that determines a target parameter group related to calculating the behavior of the virtual object in the virtual space based on a relationship between the physical behavior data and the virtual behavior data.

[0007] A program according to another aspect of the present disclosure causes a computer to perform the following actions: acquire physical behavior data relating to the behavior of a physical object in a physical space; acquire virtual behavior data relating to the behavior of a virtual object corresponding to the physical object in the virtual space, which is generated based on a set of parameters relating to the virtual space; and determine a set of target parameters relating to calculating the behavior of the virtual object in the virtual space based on the relationship between the physical behavior data and the virtual behavior data.

[0008] An information processing method according to another aspect of the present disclosure involves a computer performing the following actions: acquiring physical behavior data relating to the behavior of a physical object in a physical space; acquiring virtual behavior data relating to the behavior of a virtual object corresponding to the physical object in the virtual space, which is generated based on a set of parameters relating to a virtual space; and determining a set of target parameters relating to calculating the behavior of the virtual object in the virtual space based on the relationship between the physical behavior data and the virtual behavior data. [Effects of the Invention]

[0009] According to this disclosure, it is possible to improve the quality of the behavior of objects experienced by users in virtual space. [Brief explanation of the drawing]

[0010] [Figure 1] This diagram illustrates the general operation of the information processing device 2 in this embodiment. [Figure 2] This is a diagram illustrating an example of the functional configuration of System 1 in this embodiment. [Figure 3] This is a diagram illustrating an example of the operation of the information processing device 2 of this embodiment. [Figure 4] This is a diagram illustrating an example of the operation of the information processing device 2 of this embodiment. [Figure 5]This is a diagram illustrating an example of the operation of the information processing device 2 of this embodiment. [Figure 6] This is a diagram illustrating an example of the operation of the information processing device 2 of this embodiment. [Figure 7] This is a diagram illustrating an example of the operation of the information processing device 2 of this embodiment. [Figure 8] This is a diagram illustrating an example of the operation of the information processing device 2 of this embodiment. [Figure 9] This is a diagram illustrating an example of the operation of the information processing device 2 of this embodiment. [Figure 10] This is a diagram illustrating an example of the operation of the information processing device 2 of this embodiment. [Figure 11] This is a diagram illustrating an example of the operation of the information processing device 2 of this embodiment. [Figure 12] This diagram illustrates an example of the hardware configuration of each device included in System 1 of this embodiment. [Modes for carrying out the invention]

[0011] 1. Overview Conventionally, technologies related to XR (Extended Reality), including VR (Virtual Reality), AR (Augmented Reality), and MR (Mixed Reality), are known. In this embodiment, the space that a user can experience using XR technology is referred to as "virtual space," but the virtual space is not limited to this and may include environments that the user can perceive as two-dimensional and / or three-dimensional space in other ways. The virtual space may be something that the user can experience using a body-worn device such as an HMD (Head Mount Display). Also in this embodiment, the space in which the user's physical body exists is referred to as "physical space."

[0012] In general, physical laws different from those in physical space can be realized in virtual space. In one example, a space with lower gravity than physical space can be realized in virtual space. In another example, a space with no friction between objects can be realized in virtual space.

[0013] In contrast, as one example of application of XR-related technologies, physical laws, arrangement of objects, etc. in physical space can be reproduced in virtual space, allowing a user to experience simulation and training assuming the physical space through the virtual space. According to such technology, a user can apply experience gained in virtual space to movements in physical space. In such technology, it is desirable that the behavior of objects in physical space is sufficiently reproduced in the behavior of objects in virtual space. This is because if there is a discrepancy between the behavior of an object in virtual space and the behavior of the object in physical space, it becomes difficult for the user to apply the experience gained in virtual space to movements in physical space, even if the user accumulates such experience. Furthermore, such a discrepancy is assumed to cause the user to incorrectly learn movements different from those that should originally be taken in physical space while in virtual space.

[0014] According to the information processing device 2 according to the present embodiment (hereinafter simply referred to as "information processing device 2"), the problem according to the above example can be solved. An outline of the operation of the information processing device 2 will be described with reference to FIG. 1.

[0015] First, the information processing device 2 acquires physical behavior data related to the behavior of a physical object in physical space (S1). The user u, ball b, and goal g in FIG. 1 are all examples of physical objects in physical space. Data relating to the trajectory of the ball b and data indicating whether the ball b entered the goal g are both examples of physical behavior data.

[0016] Next, the information processing device 2 acquires virtual behavior data relating to the behavior of a virtual object corresponding to a physical object in a virtual space, the virtual behavior data being generated based on a parameter group relating to the virtual space (S2). The virtual user vu, virtual ball vb, and virtual goal vg in FIG. 1 are all examples of virtual objects corresponding to physical objects (the user u, the ball b, and the goal g) in the virtual space. Data relating to the trajectory of the virtual ball vb and data indicating whether or not the virtual ball vb entered the virtual goal vg are both examples of the virtual behavior data.

[0017] In the example of FIG. 1, assume that in the physical space (upper left of FIG. 1), the ball b kicked by the user u enters the goal g. In contrast, assume that in the virtual space (lower left of FIG. 1), although the virtual user vu kicked the virtual ball vb at the same initial velocity and the same angle as in the physical space, the virtual ball vb did not enter the virtual goal vg. This can be considered to be caused by the fact that the parameter group for generating the virtual behavior data (for example, the coefficient of friction set for the virtual ball vb, the air resistance set in the virtual space, etc.) does not reproduce the behavior of the ball b and the like in the physical space.

[0018] Accordingly, the information processing device 2 determines a target parameter group related to calculating the behavior of a virtual object in the virtual space based on the relationship between physical behavior data and the virtual behavior data (S3). In one embodiment, the information processing device 2 determines the target parameter group such that a difference between the physical behavior data and the virtual behavior data is reduced.

[0019] The information processing device 2 can apply the determined target parameter group to the virtual space (S4). In one example, this causes a virtual ball vb released by a virtual user vu with the same initial velocity and angle as in the physical space to enter the virtual goal vg. Applying the parameter group to the virtual space includes making the function and / or device that generates virtual behavior data capable of generating virtual behavior data based on the parameter group.

[0020] Thus, according to the information processing device 2, user u can experience simulations and training in a virtual space in which the behavior of objects in physical space is reproduced with high accuracy. This is one example of improving the quality of the behavior of objects experienced by the user in a virtual space, but it is not limited to this.

[0021] The information processing device 2 according to this embodiment will be described in detail below with reference to Figures 2 to 12.

[0022] In this embodiment, terms described with prefixes such as "first," "nth," and "nth" are not necessarily limited to a specific order. For example, the "first parameter group" included in a group of N sets of parameters does not necessarily refer to the first set of the N sets of parameters, but may refer to any one of the N sets of parameters. Similarly, the "second parameter group" included in a group of N sets of parameters does not necessarily refer to the parameter group following the "first parameter group." In another example, the "Kth parameter" included in a parameter group containing K parameters does not necessarily refer to the last parameter of the parameter group, but may refer to any one of the parameters included in the parameter group.

[0023] However, for the sake of explanation, in the following, terms beginning with "1st" and terms beginning with "2nd" may be presented as if they were adjacent. Additionally, for example, when referring collectively to terms beginning with "1st," "2nd," "3rd," ..., "N-1st," and "Nth," for the sake of explanation, these may be grouped together in the form "1st to Nth."

[0024] 2. Functional Configuration Referring to Figure 2, the functional configuration of System 1 of this embodiment (hereinafter simply referred to as "System 1") will be described. System 1 includes an information processing device 2, an observation device 3, a virtual space generation device 4, a terminal device 5, and a communication network 6. The information processing device 2, the observation device 3, the virtual space generation device 4, and the terminal device 5 are configured to communicate with each other via the communication network 6.

[0025] 2.1 Observation device 3 Observation device 3 observes the behavior of physical objects in physical space and generates physical behavior data. In one embodiment, observation device 3 may include distance sensors, angle sensors, motion capture sensors, velocity sensors, pressure sensors, wind speed sensors, cameras, and microphones. In one example, the physical behavior data may include data relating to the velocity, direction of movement (angle of movement), position, and size of the physical object. In another example, the physical behavior data may include data relating to the force exerted by the physical object on other physical objects (for example, the pressure when ball b in Figure 1 collides with goal g). In yet another example, the physical behavior data may include data relating to sounds emitted spontaneously by the physical object or through interaction with other physical objects.

[0026] 2.2 Terminal device 5 Terminal device 5 is a communication device used by users experiencing the virtual space. Examples of terminal devices 5 include HMDs, smartphones, personal computers, tablet devices, and wearable devices. Terminal device 5 includes an input interface, an output interface, and a communication interface.

[0027] The input interface is an interface for the terminal device 5 to receive input from the user. The input interface may be a touch panel, microphone, camera, keyboard, mouse, etc. The output interface is an interface for transmitting information to the user via images, sound, etc. The output interface may be a display, speaker, etc. The communication interface is an interface for realizing communication with other devices via the communication network 6. The communication interface may be a wireless communication interface or a wired communication interface.

[0028] 2.3 Virtual Space Generation Device 4 The virtual space generator 4 generates data for the user to experience the virtual space. The user may experience the virtual space by using the terminal device 5. The virtual space generator 4 may allow the user to download the data for experiencing the virtual space to the terminal device 5, and may provide the data to the terminal device 5, for example, in a streaming format.

[0029] In one embodiment, the data for experiencing the virtual space may include a set of parameters for calculating virtual behavior data. The set of parameters includes one or more parameters. Each parameter may be a value associated with a certain item. As illustrated in the example in Figure 1, the static friction coefficient, kinetic friction coefficient, coefficient of restitution, drag force (in one example, the force due to air resistance), position, angle, and size of the virtual ball vb and virtual goal vg are examples of parameters. The virtual space may be configured with, for example, the virtual ball vb and virtual goal vg as a single virtual object, or it may be configured with multiple virtual objects. Furthermore, if, for example, the virtual ball vb and virtual goal vg are configured with multiple virtual objects, at least some of the parameters associated with each virtual object may be the same, or at least some may be different. The virtual space generation device 4 can generate virtual behavior data based on the set of parameters.

[0030] In one embodiment, some of the parameter set may be determined based on physical behavior data. For example, the virtual space generator 4 may refer to the physical behavior data and set some of the parameter set so that the position and velocity of the virtual ball vb at a particular time point are the same as the position and velocity of the ball b in the physical space at the time corresponding to that particular time point, and then generate virtual behavior data. This makes it easier to identify inconsistencies (i.e., areas for improvement in the parameter set) such as when, for example as shown in Figure 1, the virtual ball vb is released in the virtual space under the same conditions as in the physical space, but does not go into the virtual goal vg.

[0031] 2.4 Information Processing Device 2 The information processing device 2 performs at least part of the processing related to improving the quality of the behavior of objects experienced by the user in the virtual space. In one embodiment, the information processing device 2 is a server device when the virtual space generation device 4 and / or observation device 3 are client devices. In one embodiment, the information processing device 2 is a cloud server device. The information processing device 2 may be a device that includes, for example, one or more virtual or physical web server devices and one or more virtual or physical database server devices.

[0032] The information processing device 2 comprises a control unit 10, a storage unit 12, a network interface unit 14, and a bus 16. The control unit 10, the storage unit 12, and the network interface unit 14 are electrically connected via the bus 16.

[0033] 2.1.1 Control Unit 10 The control unit 10 can function as a physical behavior acquisition unit 100, a virtual behavior acquisition unit 102, a determination unit 104, and an output unit 106 by executing various programs stored in the memory unit 12, which will be described later. Each function will be explained exemplified below.

[0034] 2.1.1.1 Physical behavior acquisition unit 100 The physical behavior acquisition unit 100 acquires physical behavior data relating to the behavior of a physical object in physical space. In one embodiment, the physical behavior acquisition unit 100 acquires physical behavior data generated by the observation device 3.

[0035] 2.1.1.2 Virtual Behavior Acquisition Unit 102 The virtual behavior acquisition unit 102 acquires virtual behavior data relating to the behavior of virtual objects corresponding to physical objects in the virtual space, which is generated based on a set of parameters relating to the virtual space. In one embodiment, the virtual behavior acquisition unit 102 acquires virtual behavior data generated by the virtual space generation device 4.

[0036] 2.1.1.3 Determination Section 104 The determination unit 104 determines a set of target parameters related to calculating the behavior of virtual objects in virtual space, based on the relationship between physical behavior data and virtual behavior data. In one embodiment, the determination unit 104 determines the set of target parameters based on the consistency between physical behavior data and virtual behavior data.

[0037] One example of consistency between physical behavior data and virtual behavior data is the degree of agreement between the trajectory of a physical object (e.g., ball b in Figure 1) in physical space and the trajectory of a virtual object (e.g., virtual ball vb in Figure 1) in virtual space.

[0038] Another example of consistency between physical behavior data and virtual behavior data is the degree of agreement between the results of physical interaction between a physical object and other physical objects in physical space (for example, whether ball b in Figure 1 went into goal g) and the results of virtual interaction between a virtual object and other virtual objects in virtual space (for example, whether virtual ball vb in Figure 1 went into virtual goal vg).

[0039] Another example of consistency between physical behavior data and virtual behavior data is the degree of agreement between the shape (including deformation) of a physical object when another physical object acts a force on it, and the shape (including deformation) of a virtual object when another virtual object acts a force on it.

[0040] 2.1.1.3.1 Determination of target parameters based on a brute-force approach In one embodiment, the virtual behavior data includes the nth virtual behavior data, which is generated for each natural number n from 1 to N, based on the nth set of parameters relating to the virtual space. The determination unit 104 then determines the target parameter set based on the consistency between the physical behavior data and the nth virtual behavior data for each natural number n. In one example, if the mth virtual behavior data (for a natural number 1 ≤ m ≤ N) generated based on the mth set of parameters is more consistent with the physical behavior data than any of the other virtual behavior data from the first virtual behavior data to the nth virtual behavior data, the determination unit 104 determines the mth parameter (i.e., the set of parameters that generated the mth virtual behavior data) as the target parameter.

[0041] 2.1.1.3.2 Determination of target parameters based on machine learning In one embodiment, the determination unit 104 determines the target parameter group by updating the parameter group based on the relationship between physical behavior data and virtual behavior data. In another embodiment, the determination unit 104 determines the target parameter group by updating the parameter group multiple times based on the relationship between physical behavior data and virtual behavior data.

[0042] In one embodiment, the determination unit 104 updating the parameter group includes updating the parameter group based on machine learning. Updating the parameter group based on machine learning may be updating the parameter group based on supervised learning (an example of machine learning). In the case of updating the parameter group based on supervised learning, the learning data in one example may be data in which physical behavior data (for example, data indicating the final position of a physical object in physical space) is associated with at least a portion of the virtual behavior data (for example, data indicating the initial position of a virtual object in virtual space and the forces acting on the virtual object) as the correct label.

[0043] In one embodiment, updating a set of parameters based on machine learning includes updating the set of parameters through reinforcement learning based on the relationship between physical behavior data and virtual behavior data, where a higher consistency between the physical behavior data and the updated virtual behavior data is associated with a higher reward.

[0044] Generally, in reinforcement learning, an agent with a policy observes a state and outputs an action in response to that observation. The state changes in response to this action, and a reward is associated with the changed state. The agent updates its policy according to this reward. In one embodiment, the agent's "action" corresponds to outputting a set of parameters. In another embodiment, the "state" corresponds to the consistency between virtual behavior data, generated based on the outputted set of parameters, and physical behavior data.

[0045] In one example, the decision unit 104 updates a certain set of parameters to a first set of parameters, and if the first virtual behavior data generated based on the first set of parameters (an example of updated virtual behavior data) has high consistency with the physical behavior data, it updates the reinforcement learning model by associating a high reward. In another example, the decision unit 104 updates a certain set of parameters to a second set of parameters, and if the second virtual behavior data generated based on the second set of parameters (an example of updated virtual behavior data) has low consistency with the physical behavior data, it updates the reinforcement learning model by associating a small reward.

[0046] In one embodiment, the decision unit 104 updating the parameter group includes repeatedly updating the parameter group by reinforcement learning until the consistency between the physical behavior data and the updated virtual behavior data satisfies a predetermined condition. In one example, the predetermined condition may be that the degree of agreement (an example of consistency) between the physical behavior data and the updated virtual behavior data exceeds a predetermined threshold. The decision unit 104 may stop updating the parameter group even if the predetermined condition is not satisfied, for example, after updating the parameter group a predetermined number of times.

[0047] 2.1.1.3.3 Determination of target parameters based on optimization In one embodiment, the determination unit 104 takes a set of parameters as input and determines a set of target parameters based on optimization, with an objective function that outputs the consistency between virtual behavior data corresponding to the set of parameters and physical behavior data. For example, when both the physical object and the virtual object perform relatively simple movements (for example, when a user drops the physical object and the virtual object from a certain height to the floor), the above-mentioned objective function can sometimes be mathematically formulated analytically and / or approximately. The determination unit 104 can identify such a formula and, based on that formula, determine a set of parameters as the target parameter set that generates virtual behavior data consistent with the physical behavior data.

[0048] In one embodiment, the optimization performed by the decision unit 104 includes black-box optimization. When physical and virtual objects undergo relatively complex motion, it is generally difficult to mathematically and / or approximately formulate the objective function described above. In such cases, the decision unit 104 may determine the target parameters by treating the objective function as unknown and, for example, by black-box optimization based on Bayesian optimization.

[0049] 2.1.1.3.4 Determination of target parameters based on exploration In one embodiment, the parameter group includes a first parameter, and the determination of the target parameter group by the determination unit 104 includes determining the first parameter by applying a search algorithm to one or more candidate parameters, including a candidate for the first parameter. Applying a search algorithm to one or more candidate parameters may include excluding at least some of the one or more candidate parameters from the candidates for the first parameter based on information regarding the consistency between the physical behavior data and the virtual behavior data when at least one of the one or more candidate parameters is set (assumed) as the first parameter.

[0050] In one embodiment, determining the first parameter by applying a search algorithm includes the following processes (a1) to (a4). (a1) Obtain information regarding the consistency between physical behavior data and virtual behavior data when the first parameter is set to one or more candidate parameters between the kth candidate parameter with a relatively small value and the lth candidate parameter with a relatively large value. (a2) Identify one or more candidate parameters between the k-th candidate parameter and the l-th candidate parameter that have a higher consistency compared to the other candidate parameters. (a3) Based on the candidate parameters identified in (a2) above, determine the k1 candidate parameter from the k-th candidate parameter to the l-th candidate parameter, which is greater than or equal to the k-th candidate parameter and less than or equal to the candidate parameter identified in (a2) above, and the k2 candidate parameter, which is greater than or equal to the candidate parameter identified in (a2) above and less than or equal to the l-th candidate parameter. (a4) Determine the first parameter between candidate parameter k1 and candidate parameter k2.

[0051] In one embodiment, the determination unit 104 further determines one or more candidate parameters between the k-th candidate parameter and the l-th candidate parameter by dividing the interval into K divisions. For example, if the k-th candidate parameter is "1", the l-th candidate parameter is "10", and the number of divisions is "9", then there may be 10 candidate parameters, namely "1, 2, 3, ..., 9, 10".

[0052] In one example, the k1 candidate parameter can be calculated based on the following formula. Candidate parameter k1 = Candidate parameter with higher consistency compared to other candidate parameters - (Candidate parameter k + Candidate parameter l) ÷ (Number of divisions between candidate parameter k and candidate parameter l)

[0053] In one example, the k2 candidate parameter can be calculated based on the following formula. The k2nd candidate parameter = Candidate parameter with higher consistency compared to other candidate parameters + (kth candidate parameter + lth candidate parameter) ÷ (number of divisions between the kth and lth candidate parameters)

[0054] An example of the operation of the determination unit 104 will be explained with reference to Figures 3 to 5. Figure 3 is a diagram showing the k-th candidate parameter, the l-th candidate parameter, and one or more candidate parameters on a number line conceptually representing the first parameter. The first parameter can take any value from one or more candidate parameters from the k-th candidate parameter to the l-th candidate parameter. First, the determination unit 104 obtains information regarding the consistency between the physical behavior data and the virtual behavior data when the first parameter is set to each of the one or more candidate parameters (see (a1) above). That is, at this point, it can be said that the search range for the first parameter is from the k-th candidate parameter to the l-th candidate parameter.

[0055] Figure 4 is a number line conceptually representing the first parameter, showing the candidate parameter that is highly consistent with the other candidate parameters, the k1th candidate parameter, and the k2nd candidate parameter, respectively, from among one or more candidate parameters. The k1th candidate parameter is greater than or equal to the kth candidate parameter and less than or equal to the candidate parameter that is highly consistent with the other candidate parameters. The k2nd candidate parameter is greater than or equal to the candidate parameter that is highly consistent with the other candidate parameters and less than or equal to the lth candidate parameter (see (a2) and (a3) ​​above).

[0056] Figure 5 is a diagram conceptually showing a new search range for the first parameter. After determining the k1 and k2 candidate parameters, the determination unit 104 can determine the first parameter from one or more new candidate parameters between the k1 and k2 candidate parameters.

[0057] Comparing Figures 3 and 5, it can be seen that the search range for the first parameter in Figure 5 (from the k1th candidate parameter to the k2nd candidate parameter) is narrower than the search range for the first parameter in Figure 3 (from the kth candidate parameter to the lth candidate parameter). Therefore, this method allows for a narrower search range when determining the first parameter.

[0058] In one embodiment, the number of one or more candidate parameters between the kth candidate parameter and the lth candidate parameter is the same as the number of one or more candidate parameters between the k1th candidate parameter and the k2nd candidate parameter. In one example, the determination unit 104 may set the number of candidate parameters between the kth candidate parameter and the lth candidate parameter to 10 (see (a1) above), and then similarly set the number of candidate parameters between the k1st candidate parameter and the k2nd candidate parameter to 10 (see (a2) and (a3) ​​above). Since the interval between the k1st candidate parameter and the k2nd candidate parameter is narrower than the interval between the kth candidate parameter and the lth candidate parameter, the step size of the search can be gradually reduced by keeping the number of candidate parameters the same.

[0059] In one embodiment, the determination unit 104 can gradually narrow the search range for the first parameter by recursively performing the processes from (a1) to (a4) described above. More specifically, in one embodiment, the determination of the first parameter by the determination unit 104 between the k1 candidate parameter and the k2 candidate parameter includes the following processes (b1) to (b4). (b1) Obtain information regarding the consistency between physical behavior data and virtual behavior data when the first parameter is set to one or more candidate parameters between the k1 candidate parameter and the k2 candidate parameter. (b2) Identify one or more candidate parameters between the k1th candidate parameter and the k2nd candidate parameter that have a higher consistency compared to the other candidate parameters. Based on the candidate parameters identified in (b3) and (b2), determine the k3 candidate parameter from the k1 to k2 candidate parameters that is greater than or equal to the k1 candidate parameter and less than or equal to the candidate parameter identified in (b2), and the k4 candidate parameter that is greater than or equal to the candidate parameter identified in (b2) and less than or equal to the k2 candidate parameter. (b4) Determine the first parameter from among the candidate parameters k3 to k4.

[0060] Figure 6 is a number line conceptually representing the first parameter, showing the candidate parameter that has high consistency compared to other candidate parameters, the k1st candidate parameter, the k2nd candidate parameter, the k3rd candidate parameter, and the k4th candidate parameter, respectively. Figure 7 is a diagram conceptually representing a new search range for the first parameter. In one embodiment, the number of one or more candidate parameters between the k1st candidate parameter and the k2nd candidate parameter may be the same as the number of one or more candidate parameters between the k3rd candidate parameter and the k4th candidate parameter.

[0061] In one embodiment, the determination unit 104 can determine not only the first parameter but also a second parameter from the parameter group. Specifically, in one embodiment, the parameter group further includes a second parameter, and the determination of the target parameter group by the determination unit 104 includes performing the following processes (c1) to (c4) after determining the k1 candidate parameter and the k2 candidate parameter. (c1) Obtain information regarding the consistency between physical behavior data and virtual behavior data when the second parameter is set to one or more candidate parameters between the i-th candidate parameter with a relatively small value and the j-th candidate parameter with a relatively large value. (c2) Identify one or more candidate parameters between the i-th candidate parameter and the j-th candidate parameter that have a higher consistency compared to the other candidate parameters. Based on the candidate parameters identified in (c3) and (c2), determine the i1st candidate parameter from the i-th to j-th candidate parameters that is greater than or equal to the i-th candidate parameter and less than or equal to the candidate parameter identified in (c2), and the i2nd candidate parameter that is greater than or equal to the candidate parameter identified in (c2) and less than or equal to the j-th candidate parameter. (c4) Determine the second parameter between the i1st candidate parameter and the i2nd candidate parameter.

[0062] In one embodiment, the determination unit 104 may determine the first parameter based on the above process with the second parameter fixed, and then determine the second parameter based on the above process with the first parameter fixed to that value. In another embodiment, the determination unit 104 may alternately perform, for example, a process of narrowing the search range of the first parameter with the second parameter fixed, and a process of narrowing the search range of the second parameter with the first parameter fixed.

[0063] 2.1.1.4 Output section 106 The output unit 106 outputs the target parameters determined by the determination unit 104 to an external device. In one embodiment, the output unit 106 transmits the target parameters to the virtual space generation device 4.

[0064] 2.1.2 Storage section 12 The storage unit 12 stores various information necessary for the operation of the information processing device 2. In one embodiment, the storage unit 12 stores the program to be executed by the control unit 10.

[0065] 2.1.3 Network Interface Unit 14 The network interface unit 14 enables communication with other devices via the communication network 6.

[0066] 2.3 Communication Network 6 Communication network 6 enables communication between each device included in system 1. Communication network 6 enables communication between devices based, for example, on the TCP / IP protocol.

[0067] 3 operations An example of the operation of the information processing device 2 will be described with reference to Figures 8 to 11. The operation of the information processing device 2 described below may be performed by at least one of the physical behavior acquisition unit 100, the virtual behavior acquisition unit 102, the determination unit 104, and the output unit 106, or by the information processing device 2 communicating with the observation device 3 and the virtual space generation device 4.

[0068] 3.1 Example of operation based on a brute-force approach Figure 8 is a flowchart illustrating an example of the operation of the information processing device 2.

[0069] First, the information processing device 2 acquires physical behavior data (S100). Next, the information processing device 2 sets the variable n, which is a natural number, to an initial value (S102). Next, the information processing device 2 acquires the nth virtual behavior data based on the nth parameter group (S104). Next, the information processing device 2 acquires information regarding the consistency between the physical behavior data acquired in step S100 and the nth virtual behavior data acquired in the most recent step S104 (S106).

[0070] Next, the information processing device 2 determines whether the processing in steps S104 and S106 has been completed for all parameter groups (S108). If the information processing device 2 determines that these processes have not been completed for any parameter group (S108 NO), the information processing device 2 changes the variable n (S110), acquires the nth virtual behavior data based on the next nth parameter group (S104), and then executes the subsequent processing.

[0071] In response to this, if the information processing device 2 determines that the processing in steps S104 and S106 has been completed for all parameter groups (S108 YES), the information processing device 2 identifies the data from the first virtual behavior data to the Nth virtual behavior data that had the greatest consistency with the physical behavior data (S112). Then, the information processing device 2 determines the parameter group used to generate the data identified in step S112 as the target parameter (S114).

[0072] 3.2 Examples of operation based on machine learning Figure 9 is a flowchart illustrating another example of the operation of the information processing device 2.

[0073] First, the information processing device 2 acquires physical behavior data (S200). Next, the information processing device 2 determines the initial values ​​of the parameter group (S202). Next, the information processing device 2 acquires virtual behavior data based on the parameter group that was most recently determined (S204). Next, the information processing device 2 acquires information regarding the consistency between the physical behavior data acquired in step S200 and the virtual behavior data acquired in the most recent step S204 (S206).

[0074] Next, the information processing device 2 determines whether the termination condition is met (S208). If the information processing device 2 determines that the termination condition is not met (S208 NO), the information processing device 2 updates the parameter set, for example, based on reinforcement learning (S210), acquires virtual behavior data based on the updated parameter set (S204), and then executes the subsequent processes.

[0075] In contrast, if the information processing device 2 determines that the termination condition is met (S208 YES), the information processing device 2 determines the parameter group at the time of the end of the loop as the target parameter group (S212).

[0076] 3.3 Examples of operation based on optimization Figure 10 is a flowchart illustrating another example of the operation of the information processing device 2. Note that steps S300 to S310 in Figure 10 may be the same as steps S100 to S110 explained with reference to Figure 8; therefore, the following explanation will focus on the processing after the determination of "YES" in step S308.

[0077] The information processing device 2 determines an approximate model of the relationship between the parameter groups and the consistency (an example of an objective function) based on the combination of multiple parameter groups obtained by repeating steps S304 to S310 and the consistency of virtual behavior data and physical behavior data corresponding to each parameter group (S312).

[0078] Next, the information processing device 2 sets the variable k, which is a natural number, to an initial value (S314). Next, the information processing device 2 determines a set of parameters that are expected to have high consistency based on the most recently determined approximate model (S316). Next, the information processing device 2 acquires virtual behavior data based on the set of parameters determined in step S316 (S318). Next, the information processing device 2 acquires information regarding the consistency between the physical behavior data and the virtual behavior data acquired in step S318 (S320).

[0079] Next, the information processing device 2 determines whether the termination condition is met (S322). If the information processing device 2 determines that the termination condition is not met (S322 NO), the information processing device 2 updates the approximate model based on the combination of multiple parameter groups acquired in the repetition of steps S304 to S310 and steps S316 to S320, and the consistency between the virtual behavior data and physical behavior data corresponding to each parameter group (S324).

[0080] In contrast, if the information processing device 2 determines that the termination condition is met (S208 YES), the information processing device 2 determines the parameter group at the time of the end of the loop as the target parameter group (S314).

[0081] 3.4 Example of operation based on exploration Figure 11 is a flowchart illustrating another example of the operation of the information processing device 2.

[0082] First, the information processing device 2 acquires physical behavior data (S400). Next, the information processing device 2 divides the search range of the first parameter from the parameter group into K segments (S402).

[0083] Next, the information processing device 2 sets the variable k, which is a natural number, to an initial value (S404). Next, the information processing device 2 sets the first parameter to the k-th candidate parameter and acquires virtual behavior data while keeping the second parameter fixed (S406). Next, the information processing device 2 acquires information regarding the consistency between the physical behavior data and the virtual behavior data acquired in step S406 (S408).

[0084] Next, the information processing device 2 determines whether the processing in steps S406 and S408 has been completed for all candidate parameters (S410). If the information processing device 2 determines that these processes have not been completed for any candidate parameter (S410 NO), the information processing device 2 changes the variable k (S412), acquires virtual behavior data based on the next k-th candidate parameter (S406), and then executes the subsequent processes.

[0085] In contrast, if the information processing device 2 determines that the processing in steps S406 and S408 has been completed for all candidate parameters (S410 YES), the information processing device 2 reduces the search range for the first parameter in the same manner as described with reference to Figures 3-5, for example (S414).

[0086] Next, the information processing device 2 determines whether the termination condition is met (S416). If the information processing device 2 determines that the termination condition is not met (S416 NO), the information processing device 2 further divides the search range after reduction in step S414 into K parts (S402) and executes the subsequent processing.

[0087] On the other hand, if the information processing device 2 determines that the termination condition is met (S416 YES), the information processing device 2 determines the parameter group including the first parameter at the time of termination of the loop as the target parameter group (S418). The information processing device 2 may perform the processing from step S400 to step S418 for each of the multiple parameters included in the parameter group.

[0088] 4 Hardware Configuration Referring to Figure 12, an example of a hardware configuration when the devices included in System 1 described above are implemented by computer 70 will be explained. Note that the functions of each device can also be implemented by dividing them among multiple devices.

[0089] As shown in Figure 12, the computer 70 includes a processor 700, a storage device 702, an input interface 704, a data interface 706, a communication interface 708, and a display device 710.

[0090] The processor 700 controls various processes in the computer 70 by executing programs stored in the storage device 702. For example, each functional unit of the control unit 10 of the information processing device 2 can be realized by the processor 700 executing programs stored in the storage device 702.

[0091] The storage device 702 is a storage medium such as RAM (Random Access Memory). RAM temporarily stores the program code of the program executed by the processor 700, as well as data required during program execution.

[0092] The storage device 702 can also be a non-volatile storage medium such as a hard disk drive (HDD) or flash memory. The storage device 702 stores the operating system and various programs for realizing the above configurations. The storage medium storing these various programs may be a non-transitory computer-readable medium. In addition, the storage device 702 can also store tables for registering various information and a database for managing those tables. Such programs and data are loaded into the storage device 702 as needed and accessed by the processor 700.

[0093] The input interface 704 is a device for receiving input from the user. Specific examples of the input interface 704 include cameras, buttons, microphones, keyboards, mice, touch panels, various sensors, and wearable devices. The input interface 704 may be connected to the computer 70 via an interface such as USB (Universal Serial Bus).

[0094] The data interface 706 is a device for inputting data from outside the computer 70. Specific examples of the data interface 706 include drive devices for reading data stored on various storage media. The data interface 706 may also be located outside the computer 70. In that case, the data interface 706 would be connected to the computer 70 via an interface such as USB.

[0095] The communication interface 708 is a device for communicating data with external devices of the computer 70 via a communication network 6, either wired or wirelessly. The communication interface 708 may also be located outside the computer 70. In that case, the communication interface 708 would be connected to the computer 70 via an interface such as USB.

[0096] The display device 710 is a device for displaying various types of information. Specific examples of the display device 710 include liquid crystal displays, organic EL (Electro-Luminescence) displays, and displays for wearable devices. The display device 710 may be located outside the computer 70. In that case, the display device 710 is connected to the computer 70 via, for example, a display cable. Furthermore, if a touch panel is used as the input I / F 704, the display device 710 can be configured as an integrated unit with the input I / F 704.

[0097] Furthermore, the components of the device included in the System 1 described above are such that a program stored in the storage device 702 is executed by the processor 700, thereby realizing a defined process in cooperation with other hardware. In other words, these components are conceived as both software or firmware, and as corresponding hardware, and in both concepts, they are also described and interpreted as "function," "means," "part," "processing circuit," "unit," or "module," etc.

[0098] 5 Variations The embodiments described above are provided to facilitate understanding of this disclosure and are not intended to limit it. The configurations that the embodiments may have are not limited to those exemplified and can be modified as appropriate. Furthermore, configurations shown in different embodiments can be partially substituted or combined.

[0099] The technologies described in the above embodiments do not limit the specific processing performed by the computer. For example, Figures 8 to 11 illustrate an example of the operation of the information processing device 2 with reference to a flowchart, but these do not limit the computer processing in this disclosure.

[0100] In the above embodiment, one or more candidate parameters between the k-th candidate parameter and the l-th candidate parameter were described as including the k-th candidate parameter and the l-th candidate parameter, but this is not limited to that. One or more candidate parameters between the k-th candidate parameter and the l-th candidate parameter do not have to include at least one of the k-th candidate parameter and the l-th candidate parameter itself. The same understanding can be applied to one or more candidate parameters between one candidate parameter and another as described in the above embodiment.

[0101] 6. Supplement The wording in this embodiment may be understood as follows, to the extent that it does not cause any contradiction.

[0102] In this embodiment, "performing a predetermined process based on predetermined information" may mean performing the predetermined process based on at least a portion of the predetermined information, performing the predetermined process based on at least the predetermined information, or performing the predetermined process probabilistically based on the predetermined information. In other words, "performing a predetermined process based on predetermined information" is not limited to performing the predetermined process based solely on the predetermined information.

[0103] In this embodiment, "executing another process based on a predetermined process" may mean any of the following: executing the other process after the predetermined process has been executed; executing the predetermined process and the other process consecutively; executing the other process based on information determined by the predetermined process; executing the other process on the condition that the predetermined process has been executed; or executing the other process by means of the predetermined process. Furthermore, "executing another process by a predetermined process" may be understood in the same way as "executing another process based on a predetermined process."

[0104] In this embodiment, "the predetermined information includes other information" may mean either that at least a portion of the predetermined information is the other information, or that the other information can be obtained based on the predetermined information.

[0105] In this embodiment, "a predetermined process includes other processes" may mean either that at least a part of the predetermined process is the other process (i.e., the other process is performed in the process of obtaining the result of the predetermined process), or that one aspect of the predetermined process is the other process.

[0106] In this embodiment, "a predetermined object and another object correspond" may mean that there is a one-to-one relationship between the predetermined object and the other object, that the other object is included in a predetermined set identified based on the predetermined object, or that the other object can be identified based on the predetermined object. Furthermore, "a predetermined object and another object corresponding" is not limited to being managed, for example, in a database. Also, "a predetermined object and another object being associated" may be understood in the same way as "a predetermined object and another object corresponding."

[0107] In this embodiment, "acquiring information" includes making the information processable by the control unit 10. "Acquiring information" may include, for example, receiving the information from another device, obtaining the information through predetermined processing, and reading the information from the storage unit 12.

[0108] In this embodiment, "generating information" may mean either making the information obtained by a predetermined process processable in the control unit 10, or storing the information obtained by the predetermined process in the storage unit 12.

[0109] In this embodiment, "determining information" may mean either selecting at least one piece of information from one or more pieces of information, or generating new information.

[0110] In this embodiment, "outputting information" may mean either transmitting the information to another device or outputting the information as audio or video.

[0111] 7. Example Configuration This disclosure includes the following technologies:

[0112] [Note 1] Information processing device 2 comprises: a physical behavior acquisition unit 100 that acquires physical behavior data relating to the behavior of physical objects in physical space; a virtual behavior acquisition unit 102 that acquires virtual behavior data relating to the behavior of virtual objects corresponding to physical objects in virtual space, which is generated based on a set of parameters relating to virtual space; and a determination unit 104 that determines a set of target parameters relating to the calculation of the behavior of virtual objects in virtual space based on the relationship between the physical behavior data and the virtual behavior data.

[0113] [Note 2] The virtual behavior data includes the nth virtual behavior data, generated for each natural number n from 1 to N, based on the nth set of parameters relating to the virtual space. The information processing device 2 described in Appendix 1 determines the target parameter group based on the consistency between the physical behavior data and the nth virtual behavior data for each natural number n, which is determined by the determination unit 104.

[0114] [Note 3] The information processing device 2 as described in Appendix 1 or 2, wherein the determination unit 104 determines the target parameter group by updating the parameter group based on the relationship between physical behavior data and virtual behavior data.

[0115] [Note 4] The information processing device 2 as described in Appendix 3, wherein the decision unit 104 updates the parameter group, which includes updating the parameter group based on machine learning.

[0116] [Note 5] The information processing device 2 described in Appendix 4, wherein updating the parameter set based on machine learning includes updating the parameter set by reinforcement learning based on the relationship between physical behavior data and virtual behavior data, and in reinforcement learning, a higher reward is associated with a higher consistency between the physical behavior data and the updated virtual behavior data.

[0117] [Note 6] The information processing device 2 as described in Appendix 5, wherein updating the parameter set includes repeatedly updating the parameter set by reinforcement learning until the consistency between the physical behavior data and the updated virtual behavior data satisfies a predetermined condition.

[0118] [Note 7] The information processing device 2 described in any one of the appendices 1 to 6, wherein the determination unit 104 takes a set of parameters as input and determines a set of target parameters based on optimization, the objective function of which is the consistency between virtual behavior data corresponding to the set of parameters and physical behavior data.

[0119] [Note 8] The optimization is performed by the information processing device 2 described in Appendix 7, including black-box optimization.

[0120] [Note 9] The parameter group includes a first parameter, and the determination of the target parameter group by the determination unit 104 includes determining the first parameter by applying a search algorithm to one or more candidate parameters, including a candidate for the first parameter, as described in any one of the appendices 1 to 8, the information processing device 2.

[0121] [Note 10] The information processing device 2 as described in Appendix 9, wherein determining the first parameter by applying a search algorithm includes (a1) obtaining information on the consistency between physical behavior data and virtual behavior data when the first parameter is set to one or more candidate parameters between the kth candidate parameter with a relatively small value and the lth candidate parameter with a relatively large value; (a2) identifying a candidate parameter among one or more candidate parameters between the kth candidate parameter and the lth candidate parameter that has a higher consistency compared to other candidate parameters; (a3) ​​determining a k1 candidate parameter among the kth candidate parameter to the l candidate parameter that is greater than or equal to the kth candidate parameter and less than or equal to the candidate parameter identified in (a2), and a k2 candidate parameter that is greater than or equal to the candidate parameter identified in (a2) and less than or equal to the l candidate parameter, and (a4) determining the first parameter among the k1 candidate parameter and the k2 candidate parameter.

[0122] [Note 11] The information processing device 2 as described in Appendix 10, wherein the determination unit 104 determines a first parameter among the k1st candidate parameter to the k2nd candidate parameter, which includes (b1) obtaining information on the consistency between physical behavior data and virtual behavior data when the first parameter is set to one or more candidate parameters among the k1st candidate parameter to the k2nd candidate parameter, (b2) identifying a candidate parameter among one or more candidate parameters among the k1st candidate parameter to the k2nd candidate parameter that has a higher consistency compared to other candidate parameters, (b3) determining a k3rd candidate parameter among the k1st candidate parameter to the k2nd candidate parameter, which is greater than or equal to the k1st candidate parameter and less than or equal to the candidate parameter identified in (b2), and a k4th candidate parameter among the k1st candidate parameter to the k2nd candidate parameter, based on the candidate parameter identified in (b2), and (b4) determining a first parameter among the k3rd candidate parameter to the k4th candidate parameter.

[0123] [Note 12] The information processing device 2 as described in Appendix 11, wherein the number of one or more candidate parameters between the k-th candidate parameter and the l-th candidate parameter is the same as the number of one or more candidate parameters between the k1-th candidate parameter and the k2-th candidate parameter.

[0124] [Note 13] The parameter group further includes a second parameter, and the determination of the target parameter group by the determination unit 104 includes, after determining the k1 candidate parameter and the k2 candidate parameter, (c1) obtaining information on the consistency between physical behavior data and virtual behavior data when the second parameter is set to one or more candidate parameters between the i-th candidate parameter, which has a relatively small value, and the j-th candidate parameter, which has a relatively large value; (c2) identifying a candidate parameter among one or more candidate parameters between the i-th candidate parameter and the j-th candidate parameter that has a higher consistency compared to other candidate parameters; (c3) determining an i1 candidate parameter from among the i-th candidate parameter to the j-th candidate parameter, which is greater than or equal to the i-th candidate parameter and less than or equal to the candidate parameter identified in (c2), and an i2 candidate parameter from among the i-th candidate parameter to the j-th candidate parameter, based on the candidate parameter identified in (c2); and (c4) determining a second parameter among the i1-th candidate parameter and the i2-th candidate parameter, as described in any one of appendices 10 to 12.

[0125] [Note 14] A program that causes a computer to perform the following actions: acquire physical behavior data regarding the behavior of physical objects in physical space; acquire virtual behavior data regarding the behavior of virtual objects corresponding to physical objects in virtual space, which is generated based on a set of parameters related to virtual space; and determine a set of parameters for calculating the behavior of virtual objects in virtual space based on the relationship between the physical behavior data and the virtual behavior data.

[0126] [Note 15] An information processing method in which a computer performs the following actions: acquires physical behavior data relating to the behavior of physical objects in physical space; acquires virtual behavior data relating to the behavior of virtual objects corresponding to physical objects in virtual space, which is generated based on a set of parameters relating to virtual space; and determines a set of parameters relating to the calculation of the behavior of virtual objects in virtual space based on the relationship between the physical behavior data and the virtual behavior data. [Explanation of Symbols]

[0127] 1...System, 2...Information processing device, 3...Observation device, 4...Virtual space generation device, 5...Terminal device, 10...Control unit, 12...Storage unit, 70...Computer, 100...Physical behavior acquisition unit, 102...Virtual behavior acquisition unit, 104...Decision unit, 106...Output unit, 700...Processor

Claims

1. A physical behavior acquisition unit that acquires physical behavior data regarding the behavior of physical objects in physical space, A virtual behavior acquisition unit that acquires virtual behavior data relating to the behavior of a virtual object corresponding to a physical object in the virtual space, which is generated based on a set of parameters relating to the virtual space, A determination unit that determines a set of target parameters for calculating the behavior of the virtual object in the virtual space based on the relationship between the physical behavior data and the virtual behavior data, An information processing device equipped with the following features.

2. The aforementioned virtual behavior data includes, for each natural number n from 1 to a natural number N, the nth virtual behavior data generated based on the nth set of parameters relating to the virtual space, The determination unit determines the target parameter group based on the consistency between the physical behavior data and the n virtual behavior data for each natural number n. The information processing apparatus according to claim 1.

3. The determination unit determines the target parameter group by updating the parameter group based on the relationship between the physical behavior data and the virtual behavior data. The information processing apparatus according to claim 1.

4. The determination unit updating the parameter group includes updating the parameter group based on machine learning. The information processing apparatus according to claim 3.

5. Updating the parameter set based on the machine learning includes updating the parameter set by reinforcement learning based on the relationship between the physical behavior data and the virtual behavior data, In the aforementioned reinforcement learning, a higher reward is associated with a higher consistency between the physical behavior data and the updated virtual behavior data. The information processing apparatus according to claim 4.

6. Updating the parameter set includes repeatedly updating the parameter set by reinforcement learning until the consistency between the physical behavior data and the updated virtual behavior data satisfies a predetermined condition. The information processing apparatus according to claim 5.

7. The determination unit takes the parameter group as input and determines the target parameter group based on optimization, where the objective function is the consistency between the virtual behavior data corresponding to the parameter group and the physical behavior data. The information processing apparatus according to claim 1.

8. The aforementioned optimization includes black-box optimization, The information processing apparatus according to claim 7.

9. The aforementioned group of parameters includes the first parameter, The determination of the target parameter group by the determination unit includes determining the first parameter by applying a search algorithm to one or more candidate parameters, including the candidate for the first parameter. The information processing apparatus according to claim 1.

10. Determining the first parameter by applying the search algorithm means that (a1) Obtaining information regarding the consistency between the physical behavior data and the virtual behavior data when the first parameter is set to one or more candidate parameters between the kth candidate parameter with a relatively small value and the lth candidate parameter with a relatively large value, (a2) Identifying one or more candidate parameters between candidate parameter k and candidate parameter l that have a higher consistency compared to the other candidate parameters, (a3) Based on the candidate parameters identified in (a2), determine the k1 candidate parameter from the k candidate parameter to the l candidate parameter, which is greater than or equal to the k candidate parameter and less than or equal to the candidate parameter identified in (a2), and the k2 candidate parameter, which is greater than or equal to the candidate parameter identified in (a2) and less than or equal to the l candidate parameter. (a4) Determining the first parameter between candidate parameter k1 and candidate parameter k2, including, The information processing apparatus according to claim 9.

11. The determination unit determines the first parameter between the candidate parameter k1 and the candidate parameter k2, (b1) Obtaining information regarding the consistency between the physical behavior data and the virtual behavior data when the first parameter is set to one or more candidate parameters between the k1 candidate parameter and the k2 candidate parameter, (b2) Identifying one or more candidate parameters between candidate parameter k1 and candidate parameter k2 that have a higher consistency compared to the other candidate parameters, (b3) Based on the candidate parameters identified in (b2), determine a third candidate parameter k3 from among the k1 candidate parameter to the k2 candidate parameter, which is greater than or equal to the k1 candidate parameter and less than or equal to the candidate parameter identified in (b2), and a fourth candidate parameter k4 that is greater than or equal to the candidate parameter identified in (b2) and less than or equal to the k2 candidate parameter. (b4) Determining the first parameter between candidate parameter k3 and candidate parameter k4, including, The information processing apparatus according to claim 10.

12. The number of one or more candidate parameters between candidate parameter k and candidate parameter l is the same as the number of one or more candidate parameters between candidate parameter k1 and candidate parameter k2. The information processing apparatus according to claim 11.

13. The aforementioned group of parameters further includes a second parameter, The determination unit determines the target parameter group after determining the k1 candidate parameter and the k2 candidate parameter. (c1) Obtaining information regarding the consistency between the physical behavior data and the virtual behavior data when the second parameter is set to one or more candidate parameters between the i-th candidate parameter with a relatively small value and the j-th candidate parameter with a relatively large value, (c2) Identifying one or more candidate parameters between candidate parameter i and candidate parameter j that have a higher consistency compared to the other candidate parameters, (c3) Based on the candidate parameters identified in (c2), determine the i1st candidate parameter from the i candidate parameter to the j candidate parameter, which is greater than or equal to the i candidate parameter and less than or equal to the candidate parameter identified in (c2), and the i2nd candidate parameter, which is greater than or equal to the candidate parameter identified in (c2) and less than or equal to the j candidate parameter. (c4) Determining the second parameter between the i1 candidate parameter and the i2 candidate parameter, including, The information processing apparatus according to claim 10.

14. On the computer, To acquire physical behavior data regarding the behavior of physical objects in physical space, To acquire virtual behavior data relating to the behavior of a virtual object corresponding to a physical object in the virtual space, which is generated based on a set of parameters relating to the virtual space, Based on the relationship between the physical behavior data and the virtual behavior data, a set of target parameters is determined for calculating the behavior of the virtual object in the virtual space. A program that executes something.

15. Computers To acquire physical behavior data regarding the behavior of physical objects in physical space, To acquire virtual behavior data relating to the behavior of a virtual object corresponding to a physical object in the virtual space, which is generated based on a set of parameters relating to the virtual space, Based on the relationship between the physical behavior data and the virtual behavior data, a set of target parameters is determined for calculating the behavior of the virtual object in the virtual space. An information processing method that performs [this action].

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