4 papers
M2-PALE: A Framework for Explaining Multi-Agent MCTS--Minimax Hybrids via Process Mining and LLMs
Yiyu Qian, Liyuan Zhao, Tim Miller
Monte-Carlo Tree Search (MCTS) is a fundamental sampling-based search algorithm widely used for online planning in sequential decision-making domains. Despite its success in drivin…
CollabEval: Enhancing LLM-as-a-Judge via Multi-Agent Collaboration
Yiyue Qian, Shinan Zhang, Yun Zhou +3
Large Language Models (LLMs) have revolutionized AI-generated content evaluation, with the LLM-as-a-Judge paradigm becoming increasingly popular. However, current single-LLM evalua…
Interpret Policies in Deep Reinforcement Learning using SILVER with RL-Guided Labeling: A Model-level Approach to High-dimensional and Multi-action Environments
Yiyu Qian, Su Nguyen, Chao Chen +2
Deep reinforcement learning (RL) achieves remarkable performance but lacks interpretability, limiting trust in policy behavior. The existing SILVER framework (Li, Siddique, and Cao…
Exploring Explainable Multi-agent MCTS-minimax Hybrids in Board Game Using Process Mining
Yiyu Qian, Tim Miller, Zheng Qian +1
Monte-Carlo Tree Search (MCTS) is a family of sampling-based search algorithms widely used for online planning in sequential decision-making domains and at the heart of many recent…