Synergistic Simulations: Multi-Agent Problem Solving with Large Language Models
This work addresses the challenge of enhancing multi-agent collaboration in AI simulations, which is incremental as it builds on existing LLM and simulation methods.
The paper tackled the problem of enabling multiple LLM-based agents to collaborate in simulated environments, such as a physical apartment or a programming task, to assess if they exhibit human-like synergy, with results showing improved problem-solving in group settings.
Large Language Models (LLMs) have increasingly demonstrated the ability to facilitate the development of multi-agent systems that allow the interpretation of thoughts and actions generated by each individual. Promising advancements have also been made in LLM-based interaction with existing worlds, particularly in interacting with simulated environments. This paper aims to integrate both aforementioned topics (agents & world interaction) into a single simulation where multiple agents can work together to solve a problem, modeling how groups of humans can often solve problems better than individuals. By showing whether LLMs demonstrate the synergy of human collaboration, it could lead to advancements in the applications of LLMs. We implemented two simulations: a physical studio apartment with two roommates, and another where agents collaborate to complete a programming task. We provide a multi-agent framework, discuss the performance of the agents in each simulation, and discuss potential future additions.