Application of the Monte-Carlo Tree Search to Multi-Action Turn-Based Games with Hidden Information

Abstract

Traditional search algorithms struggle when applied to complex multi-action turn-based games. The introduction of hidden information further increases domain complexity. The Monte-Carlo Tree Search (MCTS) algorithm has previously been applied to multi-action turn-based games, but not multi-action turn-based games with hidden information. This thesis compares several Monte Carlo Tree Search (MCTS) extensions (Determinized/Perfect Information Monte Carlo, Multi-Observer Information Set MCTS, and Belief State MCTS) in TUBSTAP, an open-source multi-action turn-based game, modified to include hidden information via fog-of-war.

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Document Details

Document Type
Technical Report
Publication Date
Mar 25, 2021
Accession Number
AD1134590

Entities

People

  • Connor M. Pipan

Organizations

  • Air Force Institute of Technology

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  • C4I
  • Cyber

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  • Abstracts
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  • Artificial Intelligence
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Fields of Study

  • Computer science

Readers

  • Game Theory.
  • Statistical inference.
  • Systems Analysis and Design