1 citations · 1 across the 4 of their papers we have counts for
4 papers
Particle Monte Carlo Tree Search
Yaniv Oren, Viliam Vadocz, Joery A. de Vries +3
Monte Carlo Tree Search (MCTS) is a widely used approach for policy improvement and action selection in Reinforcement Learning. Due to its sequential and deterministic nature, prin…
Combining LLMs with Logic-Based Framework to Explain MCTS
Ziyan An, Xia Wang, Hendrik Baier +6
In response to the lack of trust in Artificial Intelligence (AI) for sequential planning, we design a Computational Tree Logic-guided large language model (LLM)-based natural langu…
Enabling MCTS Explainability for Sequential Planning Through Computation Tree Logic
Ziyan An, Hendrik Baier, Abhishek Dubey +2
Monte Carlo tree search (MCTS) is one of the most capable online search algorithms for sequential planning tasks, with significant applications in areas such as resource allocation…
Decision Making in Non-Stationary Environments with Policy-Augmented Search
Ava Pettet, Yunuo Zhang, Baiting Luo +5
Sequential decision-making under uncertainty is present in many important problems. Two popular approaches for tackling such problems are reinforcement learning and online search (…