MLC-Agent: Cognitive Model based on Memory-Learning Collaboration in LLM Empowered Agent Simulation Environment

TL;DR

This paper introduces a memory-learning collaboration mechanism for agent models that enhances decision-making and adaptability in artificial society simulations by hierarchical memory management and dynamic evaluation.

Contribution

It proposes a novel hierarchical memory and multi-indicator evaluation mechanism that improves agent decision-making and realism in complex system modeling.

Findings

Agents with the new model show better decision quality.

Enhanced adaptability in simulated environments.

Improved alignment with real-world system characteristics.

Abstract

Many real-world systems, such as transportation systems, ecological systems, and Internet systems, are complex systems. As an important tool for studying complex systems, computational experiments can map them into artificial society models that are computable and reproducible within computers, thereby providing digital and computational methods for quantitative analysis. In current research, the construction of individual agent models often ignores the long-term accumulative effect of memory mechanisms in the development process of agents, which to some extent causes the constructed models to deviate from the real characteristics of real-world systems. To address this challenge, this paper proposes an individual agent model based on a memory-learning collaboration mechanism, which implements hierarchical modeling of the memory mechanism and a multi-indicator evaluation mechanism. Through hierarchical modeling of the individual memory repository, the group memory repository, and the memory buffer pool, memory can be effectively managed, and knowledge sharing and dissemination between individuals and groups can be promoted. At the same time, the multi-indicator evaluation mechanism enables dynamic evaluation of memory information, allowing dynamic updates of information in the memory set and promoting collaborative decision-making between memory and learning. Experimental results show that, compared with existing memory modeling methods, the agents constructed by the proposed model demonstrate better decision-making quality and adaptability within the system. This verifies the effectiveness of the individual agent model based on the memory-learning collaboration mechanism proposed in this paper in improving the quality of individual-level modeling in artificial society modeling and achieving anthropomorphic characteristics.