Building a Cognitive Twin using a distributed cognitive system and an evolution strategy

2025-02-03

Cognitive Systems Research

AI Summary

This paper addresses the challenge of accurately modeling and continuously adapting individual cognitive behaviors in human–machine collaboration. We propose a cognitive twin construction method grounded in a distributed cognition architecture, wherein external agents are modeled as evolvable computational twins. By integrating multi-agent simulation, real-time state synchronization, and cooperative evolutionary algorithms, the framework enables online optimization of interactive strategies. Our approach achieves the first deep coupling between distributed cognition frameworks and evolutionary strategies, supporting cross-scale generalization and 72-hour autonomous evolution without human intervention. Evaluated in industrial human–machine collaborative settings, the method attains 92% accuracy in cognitive behavior prediction and an end-to-end response latency of only 180 ms, significantly enhancing system adaptability and real-time performance.

Technology Category

Cognitive Modeling & Cognitive Systems: Simulating Human BehaviorHumans and AI: Human-Aware Planning and Behavior PredictionMultiagent Systems: Agent-Based Simulation and Emergent Behavior

Application Category

Responsible Web: Machine-in-the-loop, human agency and autonomyEconomics, Online Markets and Human Computation: Architectures and workflows that use LLMs for crowd workUser Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

Problem

• Building Cognitive Twins using distributed systems
• Training with Evolution Strategy for behavior approximation
• Automating tasks with human-like artificial agents

Innovation

• Distributed Cognitive System
• Evolution Strategy
• End-to-End Training