JP2019185074A - Simulator, simulation method and action determination device - Google Patents

Abstract

An object of the present invention is to facilitate the investigation of conditions under which a specific action such as a panic action has occurred in a crowd. An individual information storage unit stores, for a plurality of individuals, an attribute value representing a leader or a follower, a state value including a position in a space, and an action parameter acting on the state value. Each time a stimulus value is set, the probability setting unit 53 sets a first winning probability based on the stimulus value for the individual who is a follower, and sets a predetermined second winning probability for the individual who is a leader. The leader behavior determination unit 54 changes the individual for which the first winning probability is set to the leader with the probability, and sets a behavior parameter of the individual who is the leader to a value close to the achievement of the purpose. The follower action determination unit 55 changes the individual for which the second winning probability is set to a follower with the probability, and makes the behavior parameter of the individual whose attribute value is the follower similar to the surrounding individual. The individual information updating unit 56 updates the state value of each individual with the behavior parameter. [Selection] Figure 2

Description

  The present invention relates to a simulator for predicting the behavior of a crowd, a simulation method, and a behavior determining apparatus for determining the behavior of an individual who is one of the individuals constituting the crowd.

It is necessary to investigate the mechanism by which panic behavior occurs in order to properly guide the evacuation during a disaster or to design a space that can be evacuated smoothly. Therefore, there is a method using a simulator that simulates the behavior of the crowd.
Like the panic behavior that occurs in the crowd at the time of a disaster, one of the explanations of the crowd taking irrational behavior is the coherent behavior due to cognitive bias, and studies such as approaching as a leader-follower problem have been conducted. It was. The approach as a leader-follower problem is that a group of leaders who decide on their own behavior and followers who decide on their own behavior based on the behavior of others will result in irrational behavior as a result of individual interaction. This is an approach based on modeling.

  For example, in the simulation apparatus described in Patent Document 1, the relationship between a plurality of objects is represented by a hypergraph, and the behavior of the plurality of objects is simulated using the hypergraph, whereby the leader can follow a specific follower. The relationship that always takes a person and the relationship that a follower always follows a specific leader.

JP 2003-346071 A

  However, in the prior art, in order to simulate specific behavior such as panic behavior in the crowd, it is necessary to arbitrarily set the conditions for that, so it is difficult to simulate the specific behavior under unknown conditions. In addition, it was difficult to determine the conditions under which specific actions occurred.

For example, in the simulation apparatus described in Patent Document 1, a hypergraph can be generated each time the simulation is performed, but the relationship represented by the hypergraph is maintained during the simulation.
So, for example, to simulate panic behavior where evacuees are concentrated at one of the two exits, a hypergraph is created so that the majority of individuals in the crowd become followers of very few leaders, etc. It is thought that an arbitrary setting must be made to cause panic behavior.

The present invention has been made in view of the above problems, and can simulate a specific action such as a panic action in a crowd under unknown conditions, and can facilitate the investigation of a condition in which the specific action has occurred in a crowd, a simulation It is an object to provide a method and an action determination device.
In this specification, “crowd” represents a group of people, and “crowd” represents a group of social creatures including people.

  In order to achieve such an object, the present invention is a simulator for predicting the behavior of a crowd in a predetermined space capable of achieving a predetermined object, and at least for each of a plurality of individuals constituting the crowd, An attribute value that indicates whether it is a leader or a follower, a state value that includes a position in the space, and an individual information storage unit that stores a behavior parameter that acts on the state value, and a stimulus that represents the degree of stimulation that triggers the behavior A stimulus value setting unit that sequentially sets a value, and each time the stimulus value is set, the first winning probability based on the stimulus value is set for the individual whose attribute value is a follower among the individuals, A probability setting unit that sets a predetermined second winning probability for an individual whose attribute value is a leader, and the attribute value of the individual for which the first winning probability is set as the leader The behavior value of the individual whose attribute value is the leader is set to a value that brings the state value of the individual closer to the achievement of the purpose, and the position from the individual for each individual The surrounding individual information acquisition unit that extracts surrounding individuals within a predetermined distance, and the attribute value of the individual set with the second winning probability is changed to a follower with the probability, and the attribute value is a follower A follower behavior determination unit that sets a value similar to the behavior parameter of the surrounding individual of the individual to the behavior parameter of the individual, and individual information update that updates the state value of each individual with the behavior parameter of the individual And a simulator provided with a department.

  The individual information storage unit further stores a response threshold value of each individual, and the probability setting unit is higher for each individual as the stimulation value is higher and higher as the response threshold is lower. It is preferable to set the probability.

  Moreover, it is preferable that the stimulation value setting unit sets the stimulation value for each individual.

  In addition, a risk value acquisition unit that sequentially acquires a risk value that represents the magnitude of the risk that occurs in the space, the individual information storage unit is a recognition threshold that represents the magnitude of the risk that each of the individuals recognize The stimulation value setting unit stores the stimulation value set for each individual in the individual information storage unit, and the difference between the risk value and the recognition threshold of the individual is large for each individual. It is preferable to set the stimulus value by increasing the value of the individual from the value stored in the individual information storage unit.

  In addition, the stimulus value setting unit stores the stimulus value set for each individual in the individual information storage unit, and the individual information storage is performed for an individual having a larger number of the surrounding individuals extracted by the surrounding individual information acquisition unit. It is preferable to set the stimulation value by greatly increasing the value stored in the unit.

  In order to achieve such an object, the present invention is a simulation method for predicting the behavior of a crowd in a predetermined space capable of achieving a predetermined object, wherein a plurality of individuals constituting the crowd are stored in a computer. For each of the above, at least an attribute value indicating whether it is a leader or a follower, a state value including a position in the space, and a behavior parameter that acts on the state value are stored, and the degree of stimulation that triggers the behavior is represented A process of sequentially setting stimulus values, and each time the stimulus value is set, a first winning probability based on the stimulus value is set for an individual of which the attribute value is a follower, and the attribute value A process for setting a predetermined second winning probability for an individual who is a leader, and the attribute value of the individual for which the first winning probability is set to the leader with the probability In addition, a process of setting a value that brings the state value of the individual closer to the achievement of the purpose in the behavior parameter of the individual whose attribute value is a leader, and the position from the individual for each individual is a predetermined distance And the attribute value of the individual for which the second winning probability is set is changed to a follower with the probability, and the behavior parameter of the individual whose attribute value is the follower Executing a follower behavior determination unit that sets a value similar to the behavior parameter of the surrounding individual of the individual, and an individual information update unit that updates the state value of the individual with the behavior parameter of the individual A simulation method is provided.

  Further, in order to achieve such an object, the present invention is an action determination device for determining the action of an individual that is one of individuals constituting a crowd in a predetermined space capable of achieving a predetermined object, About the own individual, at least an attribute value representing whether it is a leader or a follower, a state value including a position in the space, and an individual information storage unit that stores behavior parameters that affect the state value; A stimulation value setting unit that sets a stimulation value that represents the degree of stimulation that triggers an action, and a first winning probability based on the stimulation value when the attribute value is a follower, and the attribute value is a leader A probability setting unit that sets a predetermined second winning probability in the case, and when the first winning probability is set, the attribute value is changed to a leader with the probability, and the attribute value A leader action determination unit that sets a value that brings the state value closer to the achievement of the purpose when the person is a leader, and surrounding individuals that are within a predetermined distance from the position of the individual individual among the individuals When the second winning probability is set, the surrounding individual information acquisition unit for acquiring the behavior of the surroundings is changed to the follower with the probability, and when the attribute value is the follower, There is provided a behavior determination device including a follower behavior determination unit that sets the behavior parameters similar to a behavior.

  According to the present invention, even if the same setting is performed, the leader and the follower are changed probabilistically. As a result, specific behavior such as panic behavior in the crowd can be simulated under unknown conditions, and it is possible to facilitate the investigation of the conditions that caused the specific behavior in the crowd.

1 is a block diagram illustrating a schematic configuration of a simulator 1 according to a first embodiment. It is a functional block diagram of simulator 1 concerning a first embodiment. The information which the environment information storage part 41 memorize | stores is illustrated typically. The information which the individual information storage part 42 memorizes is illustrated typically. The information which the individual information storage part 42 memorizes is illustrated typically. It is an example of an intermediate prediction result. It is another example of an intermediate prediction result. It is an example of the prediction result imaged as a graph by statistical processing. It is the flowchart which showed operation | movement of the simulator which concerns on 1st embodiment. It is a flowchart of the action determination process of the simulator which concerns on 1st embodiment.

[First embodiment]
As a first embodiment according to the simulator of the present invention, a fire has occurred in the room R having the _two exits E ₁ and E _2, and a plurality of people (crowds) who have stayed in the room R can pass through any of the exits. The example of the simulator 1 which estimates the action to evacuate is shown.
That is, in this embodiment, the space (target space) to be simulated is the room R, the individuals constituting the crowd are people, the purpose of the crowd or individuals is evacuation from the event of fire, and the target space Is able to achieve its purpose by having the exits E ₁ and E ₂ .
Moreover, the simulator 1 illustrated includes the behavior determination device which is another one of the present invention, and the behavior determination of the crowd is performed by the behavior determination device sequentially determining the behavior of each individual.

<Configuration of simulator 1>
FIG. 1 is a block diagram showing a schematic configuration of a simulator 1 according to the first embodiment.
The simulator 1 includes an operation input unit 2, a file input / output unit 3, a storage unit 4, a control unit 5, and a display unit 6. Of these, the file input / output unit 3, the storage unit 4, and the control unit 5 can be realized by a so-called computer, and the operation input unit 2 and the display unit 6 can be realized as peripheral devices of the computer.

  The operation input unit 2 is a user interface such as a keyboard and a mouse, and is used by a user to input data. The operation input unit 2 is connected to the control unit 5, converts a user operation into an operation signal, and outputs the operation signal to the control unit 5.

  The file input / output unit 3 is a DVD (Digital Versatile Disc) drive, a USB (Universa Serial Bus) interface, a network interface or the like, one of which is connected to an external device, a recording medium, a network, or the like not shown, and the other is a control unit 5, the data is input to the control unit 5 as a file, and the data is output from the control unit 5 as a file.

  The storage unit 4 is a memory device such as a ROM (Read Only Memory) or a RAM (Random Access Memory), and stores various programs and various data. The storage unit 4 is connected to the control unit 5 and inputs / outputs such information to / from the control unit 5.

  The control unit 5 is configured by an arithmetic device such as a CPU (Central Processing Unit), a DSP (Digital Signal Processor), or an MCU (Micro Control Unit). The control unit 5 is connected to the storage unit 4, operates as various processing units by reading and executing a program from the storage unit 4, and stores and reads various data in the storage unit 4. The control unit 5 is also connected to the operation input unit 2, the file input / output unit 3, and the display unit 6, acquires data input by the user operating the operation input unit 2, and stores the file input / output unit 3. The data is acquired and output as a file from outside, the simulation based on the earned data is performed to predict the behavior of the crowd, and the prediction result data is output from the file input / output unit 3 as a file, and / or prediction The resulting data is imaged and displayed on the display unit 6.

  The display unit 6 is a display device such as a liquid crystal display or a CRT (Cathode Ray Tube) display, and is connected to the control unit 5 and displays a prediction result by the control unit 5. The user looks at the displayed prediction result and considers the behavior of the crowd.

<Function of simulator 1>
FIG. 2 is a functional block diagram of the simulator 1 according to the first embodiment.

  The operation input unit 2 functions as a part of the condition setting unit 20. The file input / output unit 3 functions as a part of the condition setting unit 20 and a part of the prediction result output unit 57. The storage unit 4 functions as an iteration condition storage unit 40, an environment information storage unit 41, an individual information storage unit 42, and the like. The control unit 5 includes a risk value acquisition unit 50, a surrounding individual information acquisition unit 51, a stimulus value setting unit 52, a probability setting unit 53, a leader behavior determination unit 54, a follower behavior determination unit 55, an individual information update unit 56, and a prediction result output. It functions as a part of the part 57 and the like.

  The condition setting unit 20 receives input of various conditions necessary for the simulation, and causes the storage unit 4 to store the input conditions. The condition setting unit 20 is realized by, for example, the operation input unit 2 and the control unit 5 cooperating, and the control unit 5 causes the storage unit 4 to store values input by the user operating the operation input unit 2. . The condition setting unit 20 may include a file input / output unit 3 for inputting a file in which part or all of the conditions are written, and the control unit 5 selects a file designated by the user by operating the operation input unit 2. The value acquired through the input / output unit 3 and written in the file is stored in the storage unit 4.

  The simulation is performed while the virtual time is advanced within a predetermined range. In addition, trials for virtual time within the same range are repeated. Although the conditions used in each trial are the same, since the behavior of each individual at each virtual time of each trial is determined stochastically, the simulation results between trials rarely become the same.

  The virtual time simulates the passage of actual time, but is advanced every time the simulator completes the calculation for one time, and is recorded at a higher speed than the passage of actual time. For example, one virtual time is associated with an actual one second, and the virtual time is represented by an integer starting from 0 and increasing by one.

  In the simulation of the present embodiment, T is the upper limit virtual time, M is the planned number of trials, and M attempts to update environment and individual information for each virtual time while changing the virtual time from 0 to (T-1). Repeated times.

  The repetition condition storage unit 40 stores the upper limit virtual time T and the scheduled number of trials M. These values are set in advance by an input operation by the user using the condition setting unit 20.

  The environment information storage unit 41 stores information derived from the target space. Specifically, the environment information storage unit 41 stores a map of the target space including the exit area and a risk value that represents the magnitude of the risk occurring in the target space. The risk value is a function of virtual time. These values are set in advance by an input operation by the user using the condition setting unit 20.

  FIG. 3 schematically illustrates information stored in the environment information storage unit 41.

FIG. 3A illustrates a map 100 of the room R in the two-dimensional xy coordinate system. In the map 100, the room R 128 pieces in the x direction floor as the movable area of the individual, divided into blocks of 40 squares in the y-direction, a portion of the block is the exit E ₁ region, another block some have a region of the outlet E _2. Assume that the center of the room R is the origin of the xy coordinate system and the x direction is north. Environment information storage unit 41 on the grid numbers in the x-direction assigned to the center of gravity of each block, y direction grid numbers, and outlet E ₁ and outlet E ₂ and data combination of distinguishing codes and others listed Is memorized. The map 100 is a simple example for explaining the principle, but the target space may be a complex space composed of a plurality of rooms and passages. In this case, the movable area has a complicated shape excluding areas such as walls and furniture.

  FIG. 3B schematically shows the risk value. The environment information storage unit 41 stores risk value series r (0), r (1), r (2),..., R (T-1) from virtual time 0 to (T-1). In the present embodiment in which the event is a fire, the risk value is a value corresponding to the scale of the fire, and is set to be a larger value as the virtual time advances.

  The individual information storage unit 42 stores at least an attribute value, a state value, and a behavior parameter for each of a plurality of individuals constituting the crowd. The individual information storage unit 42 may further store one or more of the reaction threshold value of each individual, the recognition threshold value of each individual, and the stimulation value for each individual.

In the attribute value, an identifier indicating whether the individual is a leader or a follower is set. Leaders and followers are the role of individuals in the community. Leaders decide their actions according to their will. The follower decides his or her behavior based on the behavior of other individuals.
In the present invention, the attribute value is dynamically changed with the passage of virtual time. However, the initial value of the attribute value is common in each trial. That is, an initial value of an attribute value is set by an input operation by a user using the condition setting unit 20, and the initial value is set as the attribute value for each trial. The follower action determination unit 55 changes the information as needed.
Incidentally, in this embodiment, the initial value of the attribute value is a follower common to all individuals.

The state value represents the state of each individual at each virtual time of each trial. The state value is the position of the individual in the target space and is represented by a grid number.
The initial value of the state value is also common in each trial and is updated as the virtual time passes. That is, an initial value of the state value is set by an input operation by the user using the condition setting unit 20, and the initial value is set as the state value for each trial. The state value is set by the individual information update unit 56 described later. Updated from time to time.
Incidentally, in this embodiment, the initial value of the position of each individual is a value set at random from the grid numbers in the movable region of the room R.
In the present embodiment, the state value is only one element of the position, but in another embodiment, the state value may include a plurality of elements such as a value representing the position and orientation.

The behavior parameter represents a behavior of each individual at each virtual time of each trial, and is a parameter that affects the state value. The behavior parameter basically includes the purpose of the behavior of each individual, the behavior means determined by the individual in order to achieve the purpose, and the state value per time when the individual executes the behavior means. It is defined as a change amount (state change amount).
For example, the purpose is evacuation, and an identifier representing evacuation is stored as the purpose. However, since the purpose is one type of evacuation in this embodiment, the purpose storage may be omitted.
Further, for example, there are two ways of moving to the outlet E ₁ and moving to the outlet E ₂ , and a code representing the selected outlet is stored as the means. That is, when selecting the former, the means stores the code E ₁ , and when selecting the latter, the means stores the code E ₂ .
Further, for example, the state change amount is a movement amount in units of grids, and a set of an increment value in the x direction and an increment value in the y direction of the grid number is stored as the state change amount. That is, in the object space represented by the map of FIG. 3A, for example, when moving 1 grid north, (0, -1), when moving 1 grid northwest (-1, -1), ... Etc. are memorized. If the movement amount of the individual whose virtual time t at the trial number m is (5, 10) is (−1, −1), the virtual time (t + 1) at the trial number m is (4, 9). ) Is updated.
Incidentally, in this embodiment, the initial value of the selection exit of each individual is not defined, and the initial value of the movement amount of each individual is (0, 0). The behavior parameters are changed as needed by a leader behavior determination unit 54 and a follower behavior determination unit 55 described later.

The reaction threshold value and the recognition threshold value are parameters expressing the individuality of an individual, and are set in advance by an input operation by a user using the condition setting unit 20.
The response threshold value for each individual is a threshold value for the stimulus value of the individual, and represents a difficulty in generating a reaction that causes an action on the stimulus value, that is, a difficulty in changing to a leader. Individuals with higher response thresholds are less sensitive to stimuli and individuals with lower thresholds are more sensitive to stimuli. Therefore, an individual with a higher reaction threshold is less likely to be changed to a leader, and a lower individual is more likely to be changed to a leader.
Moreover, the recognition threshold value for each individual represents the magnitude of the risk recognized by the individual. An individual with a higher recognition threshold is less likely to increase a stimulus value in response to an increase in risk value, and an individual with a lower recognition threshold is more likely to increase the stimulus value. Therefore, an individual with a higher recognition threshold is less likely to be changed to a leader, and a lower individual is more likely to be changed to a leader.

The stimulus value is a value representing the degree of stimulus that triggers the action. For example, the stimulus value is set as a value that rises from moment to moment in accordance with, for example, an increase in the scale of a fire or a broadcast prompting evacuation. The stimulus value for each individual is a stimulus value that the individual receives at each virtual time.
In this embodiment, the stimulus value is updated as the virtual time passes. However, the initial value of the stimulation value is common in each trial. That is, an initial value of a stimulus value is set by an input operation by a user using the condition setting unit 20, and the initial value is set as the stimulus value for each trial. The stimulus value is set by a stimulus value setting unit 52 described later. Updated for each individual and for each virtual time.
Incidentally, in this embodiment, the initial value s (−1) of the stimulus value is 0 in common for all individuals.

  4 and 5 schematically illustrate information stored in the individual information storage unit 42. FIG.

An individual ID for identifying the individual is assigned to each of the N individuals, and the individual ID and information regarding the individual are stored in association with each other.
The information 110 in FIG. 4 includes the reaction threshold values θ ₀ , θ ₁ ,..., Θ _N−1 and the recognition threshold value μ stored for each individual whose individual IDs are 0, 1,. ₀ , μ ₁ ,..., Μ _N−1 are illustrated.
FIG. 5 exemplifies information 111 at the end of the simulation. As the information 111, the individual IDs at the respective time points of the virtual times 0, 1, 2,..., (T-1) in the 1, 2,. ), A history of stimulus values, a history of attribute values, a history of state values, and a history of behavior parameters are stored. For example, in the information 111, the stimulus value at each virtual time given to the individual whose individual ID is 0 in the first trial is s _1,0 (0), s _1,0 (1), s _1,0. (2), ..., s _1,0 (T-1), and corresponding to this, the attribute value of the individual in the same trial changes with follower, leader, leader, ..., leader, and in the same trial the same individual The selection exit changes to an undetermined value, E ₁ , E ₁ ,..., E _1, and the movement amount of the individual in the same trial is (0, 0), (−1, −1), (−1, −1) ),..., (-1, -1), and the grid where the individual is located in the same trial is (0, 0), (-1, -1), (-2, -2),. -64, as a result of reaching the exit _{E 1} through changes -20) is stored.

  The risk value acquisition unit 50 sequentially acquires risk values representing the magnitude of the risk occurring in the target space and outputs the risk values to the stimulus value setting unit 52. Specifically, the risk value acquisition unit 50 receives a request from the stimulus value setting unit 52 every time the virtual time t advances, reads out the risk value r (t) at the virtual time t from the environment information storage unit 41, and stimulates it. Output to the value setting unit 52.

The surrounding individual information acquisition unit 51 acquires information on surrounding individuals existing within a predetermined distance from the position of the individual for each individual, and outputs the information to the stimulus value setting unit 52 and the follower action determination unit 55.
One piece of information acquired by the surrounding individual information acquisition unit 51 is the number of surrounding individuals, and the surrounding individual information acquisition unit 51 responds to the request for specifying the individual ID from the stimulus value setting unit 52 in the vicinity of the specified individual. The number of individuals in is output. For example, preset to the radius of a circle encompassing the eighth grid in the vicinity of the distance threshold T _D. Around the individual information acquisition unit 51 refers to the individual information storage section 42, extracts an individual position in a radius T _D from the position of the specified individual is set as around the individual, the number of the extracted ambient individuals Is counted and output.
Another information acquired by the surrounding individual information acquisition unit 51 is the behavior of the surrounding individual, and the surrounding individual information acquisition unit 51 is designated in response to the request specifying the individual ID from the follower behavior determination unit 55. Output the behavior of individuals around the individual. Specifically, the surrounding individual information acquisition unit 51 extracts the surrounding individuals in the same manner as described above, and outputs the action parameters set for the extracted surrounding individuals.

The stimulus value setting unit 52 sets a stimulus value for each individual for each virtual time, and outputs the set stimulus value to the probability setting unit 53. Specifically, the stimulus value setting unit 52 sets the stimulus value s _i (t) of each individual i (individual ID i) at each virtual time t according to the following equations (1) to (4). .

However, s _i (t−1) is a stimulus value set one hour ago for the individual i, and the stimulus value setting unit 52 acquires this from the individual information storage unit 42. In addition, the stimulus value setting unit 52 stores the stimulus value set at the current time in the individual information storage unit 42 for setting after one time. δ is a stimulus increase amount per time, and a value greater than 0 is set in advance as the stimulus increase amount δ. α is a scale factor that suppresses stimulation, and a value of 0 or more is preset for the scale factor α. Further, r (t) is the risk value described above, and the stimulus value setting unit 52 acquires this from the risk value acquisition unit 50. g is a non-negative constant. μ _i is a recognition threshold for the individual i among the above-described recognition thresholds, and the stimulus value setting unit 52 acquires this from the individual information storage unit 42. n _i is the number of individuals around the individual i, and the stimulus value setting unit 52 acquires this from the surrounding individual information acquisition unit 51. N _max is the maximum number of surrounding individuals, and a value of 1 or more is set in advance.
Further, max {·, 0} in the equation (1) and “if · 0 otherwise” in the equations (2) and (4) indicate that the value is limited to non-negative.

R _{p in} equation (3) approaches 1 as the difference between the risk value r (t) and the recognition threshold μ _i increases, and the stimulus value s _i (t) in equation (1) increases greatly as R _p approaches 1. To do. That is, as the individual has a smaller recognition threshold μ _i , the difference from the risk value r (t) tends to increase and the stimulation value s _i (t) tends to increase. This models the situation in the real world where individuals with smaller cognitive thresholds are more sensitive to risk and more likely to become leaders.
Thus, the stimulus value setting unit 52 sets a stimulus value for each individual, and in particular, an individual whose difference between the risk value r (t) and the recognition threshold μ _i of the individual i is larger is stored in the individual information storage unit 42. By setting the stimulus value s _i (t) to be greatly increased from the stored value s _i (t−1), prediction closer to the real world is possible.

Also, closer to F 0 as the number _{n i} of surrounding individuals often of formula (4), equation (1) of the stimulus value _s i (t) is greatly increased as F approaches zero. That is, the stimulation value s _i (t) is likely to increase as the number of surrounding individuals increases. This is because the area where the evacuation is not progressing promotes the increase of the stimulus that triggers the action (the atmosphere that should take action is promoted), and the area where the evacuation progresses models the state of the real world where the stimulus is saturated. Yes.
In this way, the stimulus value setting unit 52 sets a stimulus value for each individual, and in particular, the individual having a larger number of surrounding individuals n _i extracted by the surrounding individual information acquisition unit 51 is stored in the individual information storage unit 42. By setting the stimulus value s _i (t) to be greatly increased from the value s _i (t−1), prediction closer to the real world is possible.

  The number of trials and the virtual time are controlled by the stimulus value setting unit 52.

  Each time the stimulus value is set by the stimulus value setting unit 52, the probability setting unit 53 sets the first winning probability based on the stimulus value for the individual whose attribute value is the follower among the individuals. The winning probability is output to the leader action determining unit 54, a predetermined second winning probability is set for the individual whose attribute value is the leader, and the second winning probability is output to the follower action determining unit 55.

Specifically, the probability setting unit 53 reads the individual ID (referred to as i) of the individual whose attribute value is the follower and the reaction threshold θ _i from the individual information storage unit 42 and input from the stimulus value setting unit 52. The stimulus value s _i (t) for the individual i is specified from the stimulus values, and the first winning probability P _i (X _i = 0 → X _i = 1) of the individual i is set according to the following equation (5).

Where X _i is a random variable corresponding to the attribute value of the individual i, X _i = 1 is that the individual i is a leader, X _i = 0 is that the individual i is a follower, and (X _i = 0 → X _i = 1) indicates that the individual i is changed from the follower to the leader. That is, P _i (X _i = 0 → X _i = 1) is the probability of changing the individual i from a follower to a leader.

The first winning probability P _i (X _i = 0 → X _i = 1) in the equation (5) is higher as the stimulus value s _i (t) is higher, and is higher as the reaction threshold θ _i is lower. This models the state of the real world where the stronger the stimulus is, the easier it is to become a leader and the more sensitive the response to the stimulus is, the easier it is to become a leader.
In this way, the probability setting unit 53 increases the first winning probability P _i (X _i = 0 → X _i = 1) as the stimulus value s _i (t) is higher for each individual and as the response threshold θ _i is lower. Set.

Further, the probability setting unit 53 specifies the individual ID (i) of the individual whose attribute value is the leader from the individual information storage unit 42, and the second winning probability P _i (X _i = 1 → X _i ) of the individual _i. = 0) is set according to the following equation (6).

Since X _i is the same as that in the expression (5), P _i (X _i = 1 → X _i = 0) is a probability of changing from a leader to a follower. A value of 0 or more is set in advance for ε.

  Equations (5) and (6) are formulated following the reaction threshold model, and the probability setting unit 53 is characterized by a unique approach that applies the reaction threshold model to the change between the leader and the follower.

  The leader behavior determination unit 54 changes the attribute value of the individual for which the first winning probability has been set by the probability setting unit 53 to the leader with the probability, and the state of the individual in the behavior parameter of the individual whose attribute value is the leader Set a value that brings the value closer to achieving the goal.

Specifically, the leader action determination unit 54 performs a lottery with the first winning probability P _i (X = 0 → X = 1) for the individual i for which the first winning probability is set, and wins the lottery. In this case, the individual information storage unit 42 stores that the attribute value X _{i of the} individual (winning individual) _i is a leader.
In the lottery, for example, the leader action determining unit 54 determines one random number in the range [0, 1], and if the random number is less than P _i (X _i = 0 → X _i = 1), the winner is determined. If P _i (X _i = 0 → X _i = 1) or more, it is determined as a loss.
In addition, the leader action determination unit 54 reads the position of the individual whose attribute value is the leader from the individual information storage unit 42, calculates the distance from the position to each exit stored in the environment information storage unit 41, The closest exit is determined as the selection exit of the winning individual.
Further, the leader action determination unit 54 calculates the distance from each of the eight neighboring grids of the position of the individual whose attribute value is the leader to the selection exit, and determines the amount of movement from the position of the individual to the nearest neighboring grid. . Then, the leader action determining unit 54 causes the individual information storage unit 42 to store the selected exit and the amount of movement determined as the purpose of the evacuation as an action parameter of the individual whose attribute value is the leader.

The follower action determination unit 55 changes the attribute value of the individual for which the second winning probability has been set by the probability setting unit 53 to the follower with the probability, and the action parameter of the individual whose attribute value is the follower Set a value similar to the behavior parameter of the individual.
Specifically, the follower action determination unit 55 performs a lottery with the second winning probability P _i (X = 1 → X = 0) for the individual i for which the second winning probability is set, and wins the lottery. attribute value X _i of the winning individual i is to be stored to be a follower to the individual information storage section 42 when.
In the lottery, for example, the follower action determination unit 55 determines one random number in the range [0, 1], and if the random number is less than P _i (X _i = 1 → X _i = 0), the winner is determined. If P _i (X _i = 1 → X _i = 0) or more, it is determined to be a loss.
Further, the follower action determination unit 55 causes the surrounding individual information acquisition unit 51 to acquire the action parameters of the individuals around the individual whose attribute value is the follower, and selects the winning individual as the most selected selection exit among the acquired action parameters. Decide on the exit.
Further, the follower action determination unit 55 calculates the distance from each of the eight neighboring grids of the position of the individual whose attribute value is the follower to the selection exit, and determines the amount of movement from the position of the individual to the nearest neighboring grid. . Then, the follower action determining unit 55 causes the individual information storage unit 42 to store the determined exit and the movement amount as the action parameters of the individual whose attribute value is the follower that the purpose is evacuation.

The individual information update unit 56 updates the state value of each individual with the behavior parameter of the individual.
In the present embodiment, the individual information update unit 56 performs the update by adding the movement amount of the behavior parameter to the position of the state value. Specifically, the individual information update unit 56 reads the position and movement amount of each individual from the individual information storage unit 42, adds the read movement amount to the read position for each individual, and sets the position after addition. The information is stored in the individual information storage unit 42.

  The prediction result output unit 57 outputs a prediction result that is a result of the simulation. The prediction result includes at least a part or all of the information stored in the individual information storage unit 42, and preferably further includes a part or all of the information stored in the environment information storage unit 41. The prediction result output unit 57 statistically processes the information stored in the individual information storage unit 42 or the information and the information stored in the environment information storage unit 41, and uses the obtained statistics as a prediction result. It may be output.

  The output timing of the prediction result may include the middle of the simulation in addition to the time when the simulation is finished. That is, part or all of the information obtained until the middle of the simulation may be output as an intermediate prediction result.

  The prediction result output unit 57 may output the prediction result as a data file, may be output as an image file, or may be displayed as an image, or in one or more of these formats. It may be output.

  Each of FIG. 6 and FIG. 7 is an example of an intermediate prediction result at a virtual time close to the upper limit virtual time at a certain number of trials. These prediction results are obtained from the same setting. The prediction result output unit 57 images the map of the target space including the exit and generates an intermediate prediction result by superimposing and drawing a figure representing the individual on the coordinates corresponding to the position of each individual on the image. 6 is output.

FIG. 8 is an example of a prediction result obtained by imaging a statistic generated from information at the time of completing the simulation as a graph. The horizontal axis of the graph is an index value E _q representing the uniformity of the selected exit, and the vertical axis is the frequency of trials. Index value _{E q} is a value obtained by multiplying the ratio _{N r} of the ratio _{S r} and toward the north exit _{E 1} individuals individuals towards the south exit _{E 2 (E q = S r} · N r). The prediction result output unit 57 counts the data of the selection exit stored in the individual information storage unit 42 with the virtual time (T-1) for each trial exit by counting the number of individuals for each number of trials. Divide by to calculate S _r and N _r for each trial. Then, E _q is calculated by multiplying S _r and N _r for each number of trials, and E _q is counted for each of the 13 sections and divided by the scheduled number of trials to calculate the frequency for each section. Then, an image in which the frequency for each section is drawn as a graph is output to the display unit 6.

The intermediate prediction result 110 in FIG. 6 is an intermediate prediction result obtained in the course of the trial in which E _q close to 0.25 was calculated, and is the trial in the rightmost bin in the graph of FIG. is there. That is, it was predicted that in the trial in which the intermediate result of FIG. 6 was obtained, no panic occurred and evacuation was performed using the two exits almost evenly.

On the other hand, the intermediate prediction result 120 in FIG. 7 is an intermediate prediction result obtained in the course of the trial in which E _q close to 0.00 is calculated. In the graph of FIG. Is. That is, the intermediate results of Figure 7 are in the trial obtained, how the panic evacuees to the south exit E ₂ is concentrated is generated is predicted.

  For example, if the prediction result by the simulator 1 is used, for example, the analysis of comparing the position of the leader and its time transition in the trial illustrated in FIG. 6 and the trial illustrated in FIG. It becomes possible to do. Based on the results of the investigation, a plan for determining where the guidance means should be installed or where the guidance staff should be placed, and what kind of guidance the guidance means or guidance staff should perform at what timing should be formulated accurately and efficiently. It becomes possible to do.

<Operation example of simulator 1>
An operation example of the simulator 1 will be described with reference to the flowcharts of FIGS. 9 and 10.

  First, the condition setting unit 20 receives an input from the user and sets a simulation condition (step S1). That is, a simulation condition obtained by manual input by the user using the condition setting unit 20 and / or a condition setting unit 20 corresponding to the user specifying a file using the condition setting unit 20 The condition setting unit 20 sets the simulation conditions obtained by reading from the file to be stored in the repetition condition storage unit 40, the environment information storage unit 41, and the individual information storage unit.

  When the simulation conditions are set, the stimulus value setting unit 52 initializes the number of trials to 1 (step S2), and sets the loop processing of steps S2 to S12.

  Further, in the loop processing of the number of trials, the stimulus value setting unit 52 initializes the virtual time to 0 (step S3) and sets the loop processing of steps S3 to S10.

  In the virtual time loop process, first, an action determination process is performed (step S4).

  The action determination process in step S4 will be described with reference to the flowchart in FIG.

  In the action determination process, first, a risk value is acquired (step S401). That is, the stimulus value setting unit 52 requests the risk value acquisition by specifying the virtual time being set in the risk value acquisition unit 50, and the risk value acquisition unit 50 stores the risk value corresponding to the specified time in the environment information storage Read from the unit 41 and output to the stimulus value setting unit 52.

Next, the stimulus value setting unit 52 refers to the individual information storage unit 42 to identify an individual whose position has not yet reached the exit E ₁ or E ₂ , and sequentially sets the identified individual as a processing target. (Step S402), the loop processing of Steps S402 to S417 is set.

  In the loop processing of individuals, first, surrounding individuals are counted (step S403). That is, the stimulus value setting unit 52 specifies the individual ID of the individual to be processed and requests the surrounding individual information acquisition unit 51 to acquire information on the surrounding individual. The surrounding individual information acquisition unit 51 refers to the individual information storage unit 42. Then, the surrounding individuals of the designated individual are counted, and the counted number of surrounding individuals is output to the stimulus value setting unit 52.

  Next, the stimulation value is updated (step S404). That is, the stimulus value setting unit 52 reads the stimulus value and the recognition threshold value of the individual to be processed one hour before from the individual information storage unit 42, the read stimulus value, the recognition threshold value, and the risk value acquired in step S401. Then, the stimulation value is calculated by applying the number of surrounding individuals acquired in step S403 to the equations (1) to (4), and the stimulation value is added to the individual information storage unit 42.

  Subsequently, the probability setting unit 53 refers to the individual information storage unit 42 and confirms the attribute value of the individual to be processed (step S405).

  When the attribute value is a follower (YES in step S405), the probability setting unit 53 reads the reaction threshold value of the individual to be processed and the latest stimulus value from the individual information storage unit 42, and applies these to equation (5). The first winning probability is calculated (step S406). Then, the leader action determination unit 54 performs a lottery with the calculated first winning probability (step S407). When the leader action is determined (YES in step S408), the attribute value of the individual to be processed in the individual information storage unit 42 is set. Change to the leader (step S409), and the process proceeds to step 413. If not won (NO in step S408), it is determined that the attribute value of the individual to be processed is not changed, step S409 is omitted, and the process proceeds to step S415.

  On the other hand, when the attribute value is a leader (NO in step S405), the probability setting unit 53 sets the second winning probability based on the formula (6), and the follower action determining unit 55 sets the second winning probability. The lottery is performed (step S410). When it is elected (YES in step S411), the follower action determination unit 55 changes the attribute value of the individual to be processed in the individual information storage unit 42 to the follower (step S412), and the process proceeds to step S415. If not won (NO in step S411), it is determined that the attribute value of the individual to be processed is not changed, step S412 is omitted, and the process proceeds to step S413.

In step S 413, the individual behavior determining unit 54 stores, in the individual information storage unit 42, the selection exit of the individual determined to change the attribute value to the leader and the individual determined not to change the attribute value from the leader. Determine the exit closer to the stored location of the individual.
Then, the leader action determining unit 54 calculates the movement amount to the neighboring grid closest to the selection exit among the eight neighboring grids of the position of the individual to be processed as the movement amount of the individual (step S414).

On the other hand, in step S415, the follower action determination unit 55 makes the selection exit of the individual determined to change the attribute value to the follower and the individual determined not to change the attribute value from the follower similar to the surrounding individuals. To decide. The follower action determination unit 55 causes the surrounding individual information acquisition unit 51 to acquire selection outlets of individuals around the processing target individual, and determines the largest number of selection outlets among the selection outlets of the processing target individual. When the virtual time is 0 or close to 0, there are cases where many selection exits of surrounding individuals are undetermined, but in this case, the selection exits of individuals to be processed are also undetermined.
Then, the leader action determining unit 54 calculates the movement amount to the neighboring grid closest to the selection exit among the eight neighboring grids of the position of the individual to be processed as the movement amount of the individual (step S416).

  In the subsequent step S417, it is determined whether or not the individual loop processing is completed. The stimulus value setting unit 52 confirms whether or not all of the individuals that have not reached the exit have been processed. If the processing has not been completed (NO in step S417), the process returns to step S402, Continue the loop processing.

  On the other hand, when all of the individuals that have not reached the exit have been processed (YES in step S417), the behavior of each individual stored in the individual information storage unit 42 is determined by the leader behavior determination unit 54 and the follower behavior determination unit 55. The parameter is updated (step S418). In other words, the leader action determination unit 54 stores the selection exit determined in step S413 and the movement amount determined in step S414 in the individual information storage unit 42, and the follower action determination unit 55 determines in the selection exit determined in step S415 and step S416. The individual movement amount is stored in the individual information storage unit 42.

  When the behavior parameter is updated, the behavior determination process ends, the process proceeds to step S5 in FIG. 9, and the individual information update unit 56 uses the movement amount of each individual updated in step S4 to determine the position of the individual. Update.

  In the subsequent step S6, the prediction result is output. The prediction result output unit 57 draws the map of the room R with reference to the environment information storage unit 41, draws the latest position of each individual on the map with reference to the individual information storage unit 42, and draws the result. Are displayed on the display unit 6 as intermediate prediction results.

Subsequently, the end determination of the loop processing of the virtual time is performed. The stimulus value setting unit 52 increases the virtual time by 1 (step S7), and confirms whether all the individuals have reached the exit with reference to the individual information storage unit 42 (step S8). Furthermore, when there is an individual that has not reached the exit (NO in step S8), the stimulus value setting unit 52 checks whether or not the increased virtual time is equal to or greater than the upper limit virtual time (step S9).
When there is an individual that has not reached the exit and the virtual time is less than the upper limit virtual time (NO in step S9), the stimulus value setting unit 52 returns the process to step S4 and continues the loop process of the virtual time. .

  On the other hand, if all individuals have reached the exit (YES in step S8), or if the virtual time is equal to or greater than the upper limit virtual time (YES in step S9), the end determination of the loop processing of the number of trials is performed. The stimulus value setting unit 52 increases the number of trials by 1 (step S10), and checks whether the increased number of trials exceeds the planned number of trials (step S11). When the increased number of trials is less than or equal to the planned number of trials (NO in step S11), stimulus value setting unit 52 returns the process to step S3 and continues the loop processing of the trial number.

If the increased number of trials exceeds the planned number of trials (YES in step S11), the simulation is terminated and a prediction result is output.
Prediction result output unit 57 adds up the frequency of reference to the index value E _q individual information storage unit 42, draws a frequency graph to display the graph image drawn on the display unit 6 as a prediction result. The prediction result output unit 57 writes all data in the individual information storage unit 42 and all data in the environment information storage unit 41 to a file, and outputs the file from the file input / output unit 3 as another prediction result.

[Second Embodiment]
In the first embodiment, the individual information storage unit 42, the surrounding individual information acquisition unit 51, the stimulus value setting unit 52, the probability setting unit 53, the leader behavior determining unit 54, the follower behavior determining unit 55, and the individual information updating unit 56 are included. An example of processing for a plurality of individuals was shown. Instead of these, the simulator 1b in the second embodiment is an individual information storage unit 42b-i, a surrounding individual information acquisition unit 51b-i, a stimulus value setting unit 52b-i, each of which performs processing for one individual, The behavior determination device 70b-i including the probability setting unit 53b-i, the leader behavior determination unit 54b-i, the follower behavior determination unit 55b-i, and the individual information update unit 56b-i is provided for the number N of individuals. However, i = 0, 1, 2,..., N−1.
That is, each of the action determination devices 70b-i (where i = 0, 1, 2,..., N−1) included in the simulator 1b functions as a memory area in which the storage unit 4 is parallel or time-division, This is realized by the control unit 5 executing processes that operate in parallel or in a time-sharing manner.

  That is, each of the behavior determination devices 70b-i is a behavior determination device that determines the behavior of the individual that is one of the individuals that constitute the crowd in a predetermined space that can achieve a predetermined purpose. An individual information storage unit 42b-i that stores at least an attribute value that indicates whether the individual is a leader or a follower, a state value that includes a position in space, and a behavior parameter that affects the state value; When a stimulus value setting unit 52b-i that sets a stimulus value that represents the degree of stimulus as an opportunity, and when the attribute value is a follower, a first winning probability based on the stimulus value is set, and when the attribute value is a leader Probability setting unit 53b-i for setting a predetermined second winning probability, and when the first winning probability is set, the attribute value is changed to the leader with the probability, and the attribute value is In the case of-, the leader action determination unit 54b-i that sets a value that brings the state value close to the achievement of the goal in the action parameter, and obtains the actions of surrounding individuals that are within a predetermined distance from the position of the individual among the individuals When the second winning probability is set, the surrounding individual information acquisition unit 51b-i that changes the attribute value to the follower with the probability, and the behavior similar to the behavior of the surrounding individual when the attribute value is the follower And a follower action determination unit 55b-i for setting parameters.

[Modifications of First Embodiment and Second Embodiment]
In each said embodiment, although the environment information storage part 41 showed the example which memorize | stores the two-dimensional map of space, it is good also as a three-dimensional map. In that case, the state values and behavior parameters stored in the individual information storage units 42 and 42-i are also three-dimensional information.

  In each of the above-described embodiments and the modifications thereof, a part of the simulation condition can be set to a value according to the state of the target space in real time. For example, if a near-future situation is predicted by a simulation in which the position of an actual person is set as the position of each individual, the crowd can be guided accurately.

  In each of the above embodiments and the modifications thereof, the risk value acquisition unit 50 reads the risk value from the environment information storage unit 41. However, in another embodiment, the risk value acquisition unit 50 includes a flame sensor and smoke. You may comprise by the function of the control part 5 which processes the signal from the disaster sensor which measures the physical quantity of real space regarding disasters, such as a sensor, and a disaster sensor according to a program. In this case, the risk value acquisition unit 50 quantifies a signal indicating whether a disaster has occurred or the degree of the disaster into a risk value according to a predetermined rule.

  In each of the above-described embodiments and the modifications thereof, the individual information update units 56 and 56b-i each update the position of the individual without considering the size of the individual and eliminating the overlap. The position of the individual may be updated in consideration of the physical constraints. In this case, for example, the individual information update units 56 and 56b-i use the virtual space to determine the movement amounts determined by the leader behavior determination units 54 and 54b-i and the follower behavior determination units 55 and 55b-i, respectively. The amount of movement to a position where the individuals do not overlap is adjusted and updated. Alternatively, for example, each of the individual information update units 56 and 56b-i moves the individual closer to the selection area determined by each of the leader behavior determination units 54 and 54b-i and each of the follower behavior determination units 55 and 55b-i. The amount of movement to a position where the individuals do not overlap in the virtual space is calculated and updated.

[Third embodiment]
The autonomous mobile robot 8 according to the third embodiment includes a behavior determination device 70c that is an example of the behavior determination device of the present invention, and simulates one individual constituting a crowd.

  The autonomous mobile robot 8 includes an operation input unit 2c, a file input / output unit 3c, a control unit 5c, and a display unit similar to the operation input unit 2, the file input / output unit 3, the control unit 5 and the display unit 6 of the first embodiment. 6c, and further, a disaster sensor such as a flame sensor and a smoke sensor, a surrounding individual sensor such as a camera, a time measuring unit for measuring the actual time, and a movement control unit including a motor, wheels, positioning means, an obstacle sensor, and the like Prepare. Each of the disaster sensor, the surrounding individual sensor, the timing unit, and the movement control unit is connected to the control unit 5c. The operation input unit 2c and the display unit 6c are preferably detachable.

  The autonomous mobile robot 8 includes a condition setting unit 20, a repetition condition storage unit 40, an environment information storage unit 41, an individual information storage unit 42, a risk value acquisition unit 50, a surrounding individual information acquisition unit 51, and a stimulus value according to the first embodiment. Conditions for performing processing for one individual, corresponding to each of the setting unit 52, the probability setting unit 53, the leader behavior determining unit 54, the follower behavior determining unit 55, the individual information updating unit 56, and the prediction result output unit 57 Setting unit 20c, repetition condition storage unit 40c, environment information storage unit 41c, individual information storage unit 42c, risk value acquisition unit 50c, surrounding individual information acquisition unit 51c, stimulus value setting unit 52c, probability setting unit 53c, leader action determination unit 54c, a follower action determination unit 55c, an individual information update unit 56c, and a prediction result output unit 57c.

However, the risk value acquisition unit 50c acquires the risk value by quantifying the signal output from the disaster sensor according to a predetermined rule.
The surrounding individual information acquisition unit 51c analyzes the image output by the surrounding individual sensor, detects a robot and a person of the same type as the autonomous traveling robot 8 existing around the autonomous traveling robot 8, and detects the detected surrounding individual. The number and behavior of surrounding individuals are obtained by recognizing the number of behaviors and behavior.
The real time is used instead of the virtual time in the first embodiment, and the stimulus value setting unit 52c controls the simulation time with reference to the real time output by the time measuring unit. The number of trials is advanced for each instruction from the user through the operation input unit 2c, and the stimulus value setting unit 52c controls the number of trials with reference to the instruction signal from the operation input unit 2c.
Further, the individual information update unit 56c rotates the motor in the direction indicated by the movement amount in the behavior parameter with the distance indicated by the movement amount as an upper limit, and drives the wheels connected to the motor to move the autonomous mobile robot 8. Let Further, the individual information update unit 56c analyzes the signal from the obstacle sensor that detects obstacles around the autonomous mobile robot 8 to detect the distance and direction to the obstacle, The rotation of the motor is suppressed so that the distance is not less than a predetermined distance. In addition, the individual information update unit 56c detects a position of the autonomous mobile robot 8 by analyzing a signal output from positioning means including a communication interface that performs communication with a beacon system or a GPS system, and obtains a state value at the detected position. Update.

  Among these, the individual information storage unit 42c, the surrounding individual information acquisition unit 51c, the stimulus value setting unit 52c, the probability setting unit 53c, the leader behavior determining unit 54c, the follower behavior determining unit 55c, and the individual information updating unit 56c are the behavior determining device. 70c.

  That is, the behavior determination device 70c is a behavior determination device that determines the behavior of the individual that is one of the individuals constituting the crowd in a predetermined space in which a predetermined purpose can be achieved. , An individual value storage unit 42c that stores an attribute value that indicates whether it is a leader or a follower, a state value that includes a position in space, and a behavior parameter that affects the state value, and the degree of stimulation that triggers the behavior A stimulus value setting unit 52c for setting a stimulus value representing the value, a first winning probability based on the stimulus value when the attribute value is a follower, and a predetermined second winning probability when the attribute value is a leader If the first winning probability is set, the attribute value is changed to the leader with the probability, and the action parameter is set when the attribute value is the leader. A behavior determination unit 54c that sets a value that brings the state value close to the achievement of the target, and a surrounding individual information acquisition unit 51c that acquires the behavior of surrounding individuals within a predetermined distance from the position of the individual among the individuals When the second winning probability is set, the attribute value is changed to a follower with the probability, and when the attribute value is a follower, a follower action determination unit 55c that sets action parameters similar to the actions of surrounding individuals And comprising.

[Modifications common to the above embodiments]
In each of the above-described embodiments and modifications thereof, a room is exemplified as the target space. However, the target space may be a concert hall, a station, or the like, and is not limited to being indoors. It may be a space, or a space that crosses indoors and outdoors, such as an event venue or a school.

  In each of the above embodiments and modifications thereof, fire is exemplified as an event, but other disasters such as earthquakes and the appearance of suspicious persons, the occurrence of sick people at event venues, the appearance of celebrities, the appeal to the crowd and the presentation of questions It can also be applied to events other than disasters.

  In each of the above embodiments and modifications thereof, evacuation was exemplified as the purpose of the action, but it corresponds to an event such as a rescue action for a sick person or a field horse action, a field horse action for the appearance of a celebrity, a response to a call or a question presentation The purpose can be set as appropriate.

In each of the above-described embodiments and modifications thereof, movement is exemplified as a means of action, but a transition to a leaning posture (evacuation under a seated desk) as an evacuation means against an earthquake, a response to a call or a question presentation It is also possible to appropriately set means that does not involve movement according to the event and purpose, such as raising a hand and utterance as means. It should be noted that movement to the exit and transition to the bending posture may be set as options of means, and either of them may be selected at random.
In addition, in the said embodiment and its modification, although the means of action was two, if it is one or more, you may be other than two.

  In each of the above-described embodiments and modifications thereof, a human crowd has been exemplified, but the present invention can also be applied to other social creatures such as fish, birds and ants. When the individual is other than a person, the target space and the event can be appropriately set according to the type of the individual.

According to the simulator 1, the simulator 1b, the behavior determining device 70b-i included in the simulator 1b, the autonomous mobile robot 8 and the behavior determining device 70c included in the autonomous mobile robot 8 described above, even if the same setting is performed. Leaders and followers are changed probabilistically, and different prediction results can be generated even if the same setting is made. Therefore, specific behaviors such as panic behavior in the crowd can be predicted under unknown conditions by each of these, and using the prediction results obtained by each can facilitate the investigation of the conditions in which the specific behavior occurred in the crowd.

DESCRIPTION OF SYMBOLS 1 ... Simulator 2 ... Operation input part 20 ... Condition setting part 3 ... File input / output part 4 ... Storage part 40 ... Repetition condition storage part 41 ... Environment information storage part 42 ... Individual information storage unit 5 ... Control unit 50 ... Risk value acquisition unit 51 ... Ambient individual information acquisition unit 52 ... Stimulus value setting unit 53 ... Probability setting unit 54 ... Leader Action determining unit 55 ... Follower action determining unit 56 ... Individual information updating unit 57 ... Prediction result output unit 6 ... Display unit

Claims (7)

A simulator for predicting crowd behavior in a predetermined space capable of achieving a predetermined purpose,
For each of a plurality of individuals constituting the crowd, an individual information storage unit that stores at least an attribute value that indicates whether it is a leader or a follower, a state value that includes a position in the space, and a behavior parameter that affects the state value When,
A stimulation value setting unit for sequentially setting stimulation values representing the degree of stimulation that triggers the action;
Each time the stimulus value is set, a first winning probability based on the stimulus value is set for an individual whose attribute value is a follower among the individuals, and a predetermined first value is set for an individual whose attribute value is a leader. A probability setting section for setting a double winning probability;
The attribute value of the individual set with the first winning probability is changed to the leader with the probability, and the state value of the individual is achieved in the behavior parameter of the individual whose attribute value is the leader. A leader action determination unit that sets a value to approach,
A surrounding individual information acquisition unit that extracts a surrounding individual whose position from the individual is within a predetermined distance for each individual;
The attribute value of the individual set with the second winning probability is changed to the follower with the probability, and the behavior parameter of the individual whose attribute value is the follower is similar to the behavior parameter of the surrounding individual of the individual A follower action determination unit for setting
An individual information update unit for updating the state value of each individual with the behavior parameter of the individual;
A simulator characterized by having. The individual information storage unit further stores a reaction threshold value of each individual,
The probability setting unit sets the first winning probability higher as the stimulus value is higher for each individual and higher as the reaction threshold is lower,
The simulator according to claim 1.   The simulator according to claim 1 or 2, wherein the stimulus value setting unit sets the stimulus value for each individual. A risk value acquisition unit for sequentially acquiring risk values representing the magnitude of the risk occurring in the space,
The individual information storage unit further stores a recognition threshold value representing the magnitude of the risk recognized by each of the individuals,
The stimulus value setting unit stores the stimulus value set for each individual in the individual information storage unit, and for each individual, the individual information has a larger difference between the risk value and the recognition threshold of the individual. Setting the stimulation value by greatly increasing the value stored in the storage unit;
The simulator according to claim 3.   The stimulus value setting unit stores the stimulus value set for each individual in the individual information storage unit, and an individual having a larger number of surrounding individuals extracted by the surrounding individual information acquisition unit is stored in the individual information storage unit. The simulator according to claim 3 or 4, wherein the stimulus value is set by greatly increasing from the stored value. A simulation method for predicting crowd behavior in a predetermined space capable of achieving a predetermined purpose,
On the computer,
For each of a plurality of individuals constituting the crowd, at least an attribute value indicating whether it is a leader or a follower, a state value including a position in the space, and a behavior parameter that acts on the state value are stored.
A process of sequentially setting stimulation values representing the degree of stimulation that triggers the action;
Each time the stimulus value is set, a first winning probability based on the stimulus value is set for an individual whose attribute value is a follower among the individuals, and a predetermined first value is set for an individual whose attribute value is a leader. A process of setting the double win probability,
The attribute value of the individual set with the first winning probability is changed to the leader with the probability, and the state value of the individual is achieved in the behavior parameter of the individual whose attribute value is the leader. Processing to set the value to approach,
For each individual, a process of extracting surrounding individuals whose position from the individual is within a predetermined distance;
The attribute value of the individual set with the second winning probability is changed to the follower with the probability, and the behavior parameter of the individual whose attribute value is the follower is similar to the behavior parameter of the surrounding individual of the individual A follower action determination unit for setting
An individual information update unit for updating the state value of each individual with the behavior parameter of the individual;
A simulation method characterized by executing A behavior determination device that determines the behavior of an individual that is one of individuals constituting a crowd in a predetermined space capable of achieving a predetermined purpose,
About the own individual, at least an attribute value that indicates whether it is a leader or a follower, a state value that includes a position in the space, and an individual information storage unit that stores behavior parameters that affect the state value;
A stimulus value setting unit that sets a stimulus value that represents the degree of stimulus that triggers the action;
A probability setting unit that sets a first winning probability based on the stimulus value when the attribute value is a follower, and sets a predetermined second winning probability when the attribute value is a leader;
When the first winning probability is set, the attribute value is changed to a leader with the probability, and when the attribute value is a leader, the behavior parameter is set to a value that brings the state value closer to the achievement of the purpose. A leader action determination unit to be set,
Among the individuals, a surrounding individual information acquisition unit that acquires the behavior of surrounding individuals existing within a predetermined distance from the position of the individual,
When the second winning probability is set, the attribute value is changed to a follower with the probability, and when the attribute value is a follower, a follower action that sets the action parameter similar to the action of the surrounding individual A decision unit;
An action determination device comprising:

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