Language agents have demonstrated remarkable emergent social behaviors within simulated sandbox environments. However, the characterization of these agents has been constrained by static prompts that outline their profiles, highlighting a gap in achieving simulations that closely mimic real-life interactions.
To close this gap, we introduce MeKB-Sim, a multi-agent simulation platform based on a dynamic personal knowledge base, termed MeKB. Each agent's MeKB contains both fixed and variable attributes—such as linguistic style, personality, and memory—crucial for theory-of-mind modeling. These attributes are updated when necessary, in response to events that the agent experiences. Comparisons with human annotators show that the LLM-based attribute updates are reliable.
Based on the dynamic nature of MeKB, experiments and case study show that MeKB-Sim enables agents to adapt their planned activities and interactions with other agents effectively. Our platform includes a Unity WebGL game interface for visualization and an interactive monitoring panel that presents the agents' planning, actions, and evolving MeKBs over time.