Date of Award

3-2021

Document Type

Thesis

Degree Name

Master of Science

Department

Department of Electrical and Computer Engineering

First Advisor

Gilbert L. Peterson, PhD

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.

AFIT Designator

AFIT-ENG-MS-21-M-072

DTIC Accession Number

AD1134590

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