497 citations · 2.1k across the 297 of their papers we have counts for
8 papers · 2 filters
Reasoning, Memorization, and Fine-Tuning Language Models for Non-Cooperative Games
Yunhao Yang, Leonard Berthellemy, Ufuk Topcu
We develop a method that integrates the tree of thoughts and multi-agent framework to enhance the capability of pre-trained language models in solving complex, unfamiliar games. Th…
On The Planning Abilities of OpenAI's o1 Models: Feasibility, Optimality, and Generalizability
Kevin Wang, Junbo Li, Neel P. Bhatt +4
Recent advancements in Large Language Models (LLMs) have showcased their ability to perform complex reasoning tasks, but their effectiveness in planning remains underexplored. In t…
Human-Agent Coordination in Games under Incomplete Information via Multi-Step Intent
Shenghui Chen, Ruihan Zhao, Sandeep Chinchali +1
Strategic coordination between autonomous agents and human partners under incomplete information can be modeled as turn-based cooperative games. We extend a turn-based game under i…
Joint Verification and Refinement of Language Models for Safety-Constrained Planning
Yunhao Yang, Neel P. Bhatt, William Ward +3
Large language models possess impressive capabilities in generating programs (e.g., Python) from natural language descriptions to execute robotic tasks. However, these generated pr…
Learning to Coordinate without Communication under Incomplete Information
Shenghui Chen, Shufang Zhu, Giuseppe De Giacomo +1
Achieving seamless coordination in cooperative games is a crucial challenge in artificial intelligence, particularly when players operate under incomplete information. While commun…
Reduce, Reuse, Recycle: Categories for Compositional Reinforcement Learning
Georgios Bakirtzis, Michail Savvas, Ruihan Zhao +2
In reinforcement learning, conducting task composition by forming cohesive, executable sequences from multiple tasks remains challenging. However, the ability to (de)compose tasks…