AI RESEARCH
Toward Template-Free Explainability for Monte Carlo Tree Search
arXiv CS.AI
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ArXi:2605.16524v2 Announce Type: replace-cross Probabilistic search algorithms, such as Monte Carlo Tree Search (MCTS), have proven very effective in solving sequential decision-making tasks under uncertainty. However, interpreting asymmetric search trees that incorporate bandit-based tree traversal and simulation-based value estimation is difficult for end users based solely on raw tree statistics.