Monte-Carlo tree search using batch value of perfect information

Shahaf S. Shperberg, Eyal Shlomo Shimony, Ariel Felner

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

Abstract

This paper focuses on the selection phase of Monte- Carlo Tree Search (MCTS). We define batch value of perfect information (BVPI) in game trees as a generalization of value of computation as proposed by Russell and Wefald, and use it for selecting nodes to sample in MCTS. We show that computing the BVPI is NP-hard, but it can be approximated in polynomial time. In addition, we propose methods that intelligently find sets of fringe nodes with high BVPI, and quickly select nodes to sample from these sets. We apply our new BVPI methods to partial game trees, both in a stand-alone set of tests, and as a component of a full MCTS algorithm. Empirical results show that our BVPI methods outperform existing node-selection methods for MCTS in different scenarios.

Original languageEnglish GB
Title of host publicationUAI 2017
Subtitle of host publicationConference on Uncertainty in Artificial Intelligence
Number of pages11
StatePublished - 2017
Event33rd Conference on Uncertainty in Artificial Intelligence, UAI 2017 - Sydney, Australia
Duration: 11 Aug 201715 Aug 2017

Conference

Conference33rd Conference on Uncertainty in Artificial Intelligence, UAI 2017
Country/TerritoryAustralia
CitySydney
Period11/08/1715/08/17

ASJC Scopus subject areas

  • Artificial Intelligence

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