Lyfe Agents: Generative agents for low-cost real-time social interactions
This work addresses the problem of high computational costs for real-time human-agent interactions in virtual societies, offering a domain-specific incremental improvement.
The paper tackled the challenge of achieving real-time, low-cost social interactions with generative agents by introducing Lyfe Agents, which combine an option-action framework, asynchronous self-monitoring, and a Summarize-and-Forget memory mechanism, resulting in a computational cost 10-100 times lower than existing alternatives.
Highly autonomous generative agents powered by large language models promise to simulate intricate social behaviors in virtual societies. However, achieving real-time interactions with humans at a low computational cost remains challenging. Here, we introduce Lyfe Agents. They combine low-cost with real-time responsiveness, all while remaining intelligent and goal-oriented. Key innovations include: (1) an option-action framework, reducing the cost of high-level decisions; (2) asynchronous self-monitoring for better self-consistency; and (3) a Summarize-and-Forget memory mechanism, prioritizing critical memory items at a low cost. We evaluate Lyfe Agents' self-motivation and sociability across several multi-agent scenarios in our custom LyfeGame 3D virtual environment platform. When equipped with our brain-inspired techniques, Lyfe Agents can exhibit human-like self-motivated social reasoning. For example, the agents can solve a crime (a murder mystery) through autonomous collaboration and information exchange. Meanwhile, our techniques enabled Lyfe Agents to operate at a computational cost 10-100 times lower than existing alternatives. Our findings underscore the transformative potential of autonomous generative agents to enrich human social experiences in virtual worlds.