Digital Twin Action Selection Model for Mixed Group Simulations
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Solution Overview
Problem
Existing multi-agent simulations struggle to accurately express selection tendencies when individuals and groups are mixed, leading to decreased simulation accuracy due to the inability to account for varying group sizes and their impact on action selection.
Innovation Solution
The simulation apparatus specifies the number of members in each group and incorporates this attribute into the action selection model, using a multi-agent simulation to accurately predict action changes in response to measures, such as toll road installations, by training itinerary selection models with latent variables representing group structures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a discrete selection model is used for action selection in multi-agent simulation, then the simulation can be performed with simpler computational procedures, but the simulation accuracy decreases when individuals and groups are mixed because the model cannot correctly express selection tendencies
Solution Approach 1:
The patent changes the parameter structure of the action selection model by introducing group size as a new parameter. Instead of using a simple discrete selection model that treats all agents uniformly, the modified model incorporates group size information to adjust selection probabilities. This allows the system to maintain computational simplicity while improving simulation accuracy for mixed individual-group scenarios.
2Measurement precision
If group size information is incorporated into the action selection model, then simulation accuracy improves by correctly expressing selection tendencies, but the model complexity increases
Solution Approach 1:
The patent applies local quality by making the action selection model sensitive to local group size characteristics. Instead of using a uniform model for all agents, the system adjusts selection probabilities based on the specific group size of each agent. This localized adaptation improves simulation accuracy without requiring complete redesign of the entire simulation framework.
3Measurement precision
If a detailed action selection model accounting for group structures is used, then prediction of measure effects becomes more precise, but computational load increases
Solution Approach 1:
The patent applies partial action by incorporating only the essential group size information into the action selection model rather than implementing a complete complex model. This selective approach captures the most important factor (group size) that affects selection tendencies while avoiding the computational burden of modeling all possible group structure variations.
Data Source
AI summary
A simulation apparatus generates a digital twin in which the real world is reproduced in a virtual space. The simulation apparatus specifies, in the generated digital twin, an agent corresponding to a person existing in the real world. The simulation apparatus simulates an action of the person based on composition information of the specified agent and an action selection model for predicting action selection of the person.