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Evaluating Theory of Mind and Internal Beliefs in LLM-Based Multi-Agent Systems

Adam Kostka, Jarosław A. Chudziak

TL;DR

A novel multi-agent architecture integrating ToM, BDI-style internal beliefs, and symbolic solvers for logical verification is introduced and evaluated in a resource allocation problem with various LLMs and finds an intricate interaction between LLM capabilities, cognitive mechanisms, and performance.

Abstract

LLM-based MAS are gaining popularity due to their potential for collaborative problem-solving enhanced by advances in natural language comprehension, reasoning, and planning. Research in Theory of Mind (ToM) and Belief-Desire-Intention (BDI) models has the potential to further improve the agent's interaction and decision-making in such systems. However, collaborative intelligence in dynamic worlds remains difficult to accomplish since LLM performance in multi-agent worlds is extremely variable. Simply adding cognitive mechanisms like ToM and internal beliefs does not automatically result in improved coordination. The interplay between these mechanisms, particularly in relation to formal logic verification, remains largely underexplored in different LLMs. This work investigates: How do internal belief mechanisms, including symbolic solvers and Theory of Mind, influence collaborative decision-making in LLM-based multi-agent systems, and how does the interplay of those components influence system accuracy? We introduce a novel multi-agent architecture integrating ToM, BDI-style internal beliefs, and symbolic solvers for logical verification. We evaluate this architecture in a resource allocation problem with various LLMs and find an intricate interaction between LLM capabilities, cognitive mechanisms, and performance. This work contributes to the area of AI by proposing a novel multi-agent system with ToM, internal beliefs, and symbolic solvers for augmenting collaborative intelligence in multi-agent systems and evaluating its performance under different LLM settings.

Evaluating Theory of Mind and Internal Beliefs in LLM-Based Multi-Agent Systems

TL;DR

A novel multi-agent architecture integrating ToM, BDI-style internal beliefs, and symbolic solvers for logical verification is introduced and evaluated in a resource allocation problem with various LLMs and finds an intricate interaction between LLM capabilities, cognitive mechanisms, and performance.

Abstract

LLM-based MAS are gaining popularity due to their potential for collaborative problem-solving enhanced by advances in natural language comprehension, reasoning, and planning. Research in Theory of Mind (ToM) and Belief-Desire-Intention (BDI) models has the potential to further improve the agent's interaction and decision-making in such systems. However, collaborative intelligence in dynamic worlds remains difficult to accomplish since LLM performance in multi-agent worlds is extremely variable. Simply adding cognitive mechanisms like ToM and internal beliefs does not automatically result in improved coordination. The interplay between these mechanisms, particularly in relation to formal logic verification, remains largely underexplored in different LLMs. This work investigates: How do internal belief mechanisms, including symbolic solvers and Theory of Mind, influence collaborative decision-making in LLM-based multi-agent systems, and how does the interplay of those components influence system accuracy? We introduce a novel multi-agent architecture integrating ToM, BDI-style internal beliefs, and symbolic solvers for logical verification. We evaluate this architecture in a resource allocation problem with various LLMs and find an intricate interaction between LLM capabilities, cognitive mechanisms, and performance. This work contributes to the area of AI by proposing a novel multi-agent system with ToM, internal beliefs, and symbolic solvers for augmenting collaborative intelligence in multi-agent systems and evaluating its performance under different LLM settings.
Paper Structure (13 sections, 3 equations, 5 figures)

This paper contains 13 sections, 3 equations, 5 figures.

Table of Contents

  1. Introduction
  2. Related Work
  3. Architecture and Task Environment
  4. Task Environment Description
  5. Agent Architecture
  6. Logical Tool Integration
  7. Results and Evaluation
  8. Experimental Setup
  9. Results
  10. Analysis of Information Exchange
  11. Discussion
  12. Future Work
  13. Conclusions

Figures (5)

  • Figure 1: Internal and External Reasoning Processes of LLM Agent (based on weng2023agent).
  • Figure 2: Agents interact in a simulated game using shared and personal memory to form beliefs, make decisions, and coordinate actions based on real-time updates.
  • Figure 3: Interaction between Food, Medical, and Security agents showing information flow and exchange of their beliefs, actions, and responses.
  • Figure 4: Iterative Logic Verification Pipeline for Cognitive Agents
  • Figure 5: Bootstrapped Median Final Health Scores of districts. The bars represent the median health scores, and the error bars indicate the lower and upper bounds of the 95% confidence intervals.