Agent-Based Cognitive State Modeling for Crowd Threat Prediction
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Solution Overview
Problem
Current methods for detecting behavior in crowds are inadequate for predicting potential threats in real-time, especially in environments like airports and sporting arenas, as they lack the ability to accurately model internal cognitive states and transitions of individuals, which are crucial for early crime prevention and enhanced security.
Innovation Solution
An agent-based inference framework that uses computer vision and machine learning techniques, such as recurrent neural networks, to model internal cognitive states and predict future behaviors by synthesizing interactions and capturing social cues like location, gaze direction, and facial expressions, enabling real-time behavior recognition and classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional behavior detection methods are used in crowds, then system complexity is reduced, but measurement precision of cognitive states and behavior prediction accuracy deteriorate
Solution Approach 1:
The system segments the crowd into individual agents, each with their own cognitive state model. Each agent is tracked and analyzed separately using agent-based simulators that model internal cognitive processes independently, allowing precise measurement of individual cognitive states while managing complexity through modular agent-level processing rather than holistic crowd analysis
Solution Approach 2:
The patent introduces agent-based simulators as intermediary components that bridge observable behavior and unobservable cognitive states. These simulators act as mediators that infer internal cognitive processes from external observations, enabling accurate cognitive state estimation without requiring direct measurement of complex internal mental states
2Reliability
If real-time behavior prediction is implemented, then crime prevention effectiveness is improved, but loss of time for processing and analyzing crowd data increases
Solution Approach 1:
The system performs preliminary actions by continuously tracking and modeling cognitive states of all agents in real-time, maintaining ready-to-analyze cognitive profiles before threatening behaviors manifest. This allows the system to be prepared for rapid threat detection and response without requiring intensive processing only when threats are suspected
Solution Approach 2:
The patent implements feedback loops where predicted behaviors are continuously compared with actual observed behaviors, allowing the agent-based simulators to refine their cognitive state estimates in real-time. This feedback mechanism improves prediction accuracy for crime prevention while optimizing processing time by focusing computational resources on agents showing deviation from expected behavior patterns
3Measurement precision
If detailed cognitive state modeling is performed for each individual, then behavior prediction accuracy is improved, but device complexity and computational requirements worsen
Solution Approach 1:
The system divides the computational task into segmented agent-based simulators, each handling a single individual's cognitive state modeling. This segmentation allows detailed cognitive modeling at the individual level while distributing computational complexity across multiple independent modules, making the overall system manageable despite the detailed nature of each agent's cognitive model
Solution Approach 2:
The patent dynamically adjusts modeling parameters based on contextual needs, modifying the level of cognitive detail modeled for each agent according to situational requirements. This allows the system to maintain high prediction accuracy when needed while reducing computational complexity in situations where full cognitive modeling is not necessary
Data Source
AI summary
A security monitoring technique includes receiving data related to one or more individuals from one or more cameras in an environment. Based on the input data from the cameras, agent-based simulators are executed that each operate to generate a model of behavior of a respective individual, wherein an output of each model is symbolic sequences representative of internal experiences of the respective individual during simulation. Based on the symbolic sequences, a subsequent behavior for each of the respective individuals is predicted when the symbolic sequences match a query symbolic sequence for a query behavior.