Synergistic Simulations: Multi-Agent Problem Solving with Large Language Models

• URL: http://arxiv.org/abs/2409.13753v1
• Date: Sat, 14 Sep 2024 21:53:35 GMT
• Title: Synergistic Simulations: Multi-Agent Problem Solving with Large Language Models
• Authors: Asher Sprigler, Alexander Drobek, Keagan Weinstock, Wendpanga Tapsoba, Gavin Childress, Andy Dao, Lucas Gral,
• Abstract summary: Large Language Models (LLMs) have increasingly demonstrated the ability to facilitate the development of multi-agent systems. This paper aims to integrate agents & world interaction into a single simulation where multiple agents can work together to solve a problem. We implement two simulations: a physical studio apartment with two roommates, and another where agents collaborate to complete a programming task.
• Score: 36.571597246832326
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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