Can LLMs create social agents?
Can Agents Spontaneously Form a Society? Introducing a Novel Architecture for Generative Multi-Agent to Elicit Social Emergence
September 12, 2024
https://arxiv.org/pdf/2409.06750This paper explores whether large language model (LLM)-based agents can develop social behaviors and relationships organically. The researchers propose an improved agent architecture called ITCMA-S, designed to encourage social interactions between multiple agents in virtual environments.
Key points about ITCMA-S for LLM-based multi-agent systems:
- Social Interaction Framework: Includes the LTRHA module (Locale & Topic, Resources, Habitus, Action) to guide agents towards socially appropriate actions by managing resources and analyzing environmental and emotional cues.
- Memory Blending: Employs "conceptual blending" to combine recalled memories with current perceptions, creating richer imagined scenarios while also improving processing speed by compressing the memory.
- Emotion and Motivation: Uses a refined model where an agent's emotions (pleasure, arousal, dominance) directly influence their actions and future behavior, mimicking the role of emotions in human decision-making.
- Action Space Reduction: Implements an LLM-based elimination module to filter irrelevant actions, optimizing the decision-making process for faster task execution.