Multi-Agent Games and Applications
Organizers
Speaker
Wenyue Hua
Time
Friday, November 8, 2024 10:30 AM - 12:00 PM
Venue
A3-1-301
Online
Zoom 230 432 7880
(BIMSA)
Abstract
Can we avoid wars at the crossroads of history? This question has been pursued by individuals, scholars, policymakers, and organizations throughout human history. In this research, we attempt to answer the question based on the recent advances of Large language models and Visual language models. We propose WarAgent to simulate war outbreaking and BattleAgent to simulate battle process. They utilize LLM and VLM-powered multi-agent AI system to simulate the participating countries, their decisions, and the consequences, in historical international conflicts. By evaluating the simulation effectiveness, we examine the advancements and limitations of cutting-edge AI systems' abilities in studying complex collective human behaviors such as international conflicts under diverse settings. In these simulations, the emergent interactions among agents also offer a novel perspective for examining the triggers and conditions that lead to war. Our findings offer data-driven and AI-augmented insights that can redefine how we approach conflict resolution and peacekeeping strategies. The implications stretch beyond historical analysis, offering a blueprint for using AI to understand human history and possibly prevent future international conflicts. In the end, we will discuss how well LLMs are in game-theoretic abilities and how we can improve their ability on game-theoretic reasoning and playing.
Speaker Intro
I’m Wenyue Hua, postdoctoral researcher at University of California, Santa Barbara, working with Professor William Wang. I obtained my Ph.D. degree from Rutgers University, New Brunswick (2020 - 2024). I’m honored to be advised by Prof. Yongfeng Zhang. I received MA in Linguistics at Rutgers in 2020 (proudly advised by Prof. Adam Jardine) and BA in Linguistics and Philosophy and BS in Mathematics at UCLA in 2018 (proudly advised by Prof. Edward Keenan).
My research interests lie in Large Language Models and its various application, such as LLM-based agent, multi-agent system, LLM for social good, LLM-based recommender system, information retrieval. I care about the trustworthiness, safety, and efficiency of LLMs.