Pedestrian Agent Behavioral Objective Switching in Simulation
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Solution Overview
Problem
Conventional people flow simulations struggle to accurately reproduce human-like behaviors, particularly changes in behavioral objectives during pedestrian interactions with facilities, leading to incomplete modeling of pedestrian movements in virtual environments like shopping malls and airports.
Innovation Solution
A simulation apparatus and method that allows pedestrian agents to dynamically change their purchasing behavioral objectives based on facility search behaviors, using a simulation management unit to manage and update the agents' objectives and recognition information, simulating more humane and realistic interactions by switching between predefined categories based on set conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional people flow simulation is used with fixed behavioral objectives, then the simulation structure is simple and easy to implement, but the behavioral accuracy and realism deteriorate because agents cannot dynamically change their objectives during facility search
Solution Approach 1:
The patent implements dynamic behavioral objectives by allowing pedestrian agents to switch between different objective categories (e.g., from shopping to dining) based on real-time facility search outcomes. The simulation management unit dynamically updates agent objectives during the simulation process, transforming the static behavioral model into a dynamic one that adapts to changing conditions, thereby improving behavioral accuracy without requiring complete redesign of the simulation framework
Solution Approach 2:
The patent segments the behavioral objective into multiple categories (shopping, dining, entertainment, etc.) and allows agents to transition between these segmented states. By dividing the complex behavioral model into discrete objective categories with clear transition conditions, the system achieves higher behavioral realism while maintaining manageable simulation complexity through structured state management
2Reliability
If agents have fixed purchasing behavioral objectives throughout the simulation, then the simulation process is simple and fast, but the behavioral completeness deteriorates as it fails to capture real-world human behavior changes during facility interactions
Solution Approach 1:
The patent implements periodic evaluation of behavioral objectives at specific simulation checkpoints (when agents interact with facilities). The simulation management unit periodically checks whether agents should switch objective categories based on predefined conditions, such as after visiting a certain number of facilities or when specific time thresholds are met. This periodic objective reevaluation ensures behavioral completeness by capturing real-world behavior changes while maintaining simulation efficiency through scheduled rather than continuous evaluation
Solution Approach 2:
The patent enables agents to autonomously determine when to change their behavioral objectives based on their own facility search experiences and predefined switching conditions. Each agent independently evaluates whether to switch objective categories without requiring centralized control for every decision, thereby improving behavioral completeness through autonomous agent behavior while preserving simulation efficiency through decentralized decision-making
Data Source
AI summary
A non-transitory computer-readable recording medium has stored therein a program that causes a computer to execute a process including: inputting, to each of a plurality of agents, a plurality of purchasing behavioral objectives, a first purchasing behavioral objective, and facilities information indicating a plurality of facilities corresponding to the plurality of purchasing behavioral objectives; and causing each of the plurality of agents to determine, based on a condition that has been set in accordance with a facility search behavior with respect to a facility that is selected from the plurality of facilities in response to the purchasing behavioral objective of each of the plurality of agents at a time of execution of simulation, a second purchasing behavioral objective of each of the plurality of agents from among the plurality of purchasing behavioral objectives.