Monte Carlo Tree Search for Collaboration Control of Ghosts in Ms. Pac-Man
Kien Quang Nguyen,Ruck Thawonmas +1 more
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An application of Monte Carlo tree search (MCTS) to control ghosts in the game called Ms. Pac-Man is presented and a mechanism for predicting Ms.Pac-Man's future movements is introduced to increase the reliability of MCTS results.Abstract:
In this paper, we present an application of Monte Carlo tree search (MCTS) to control ghosts in the game called Ms Pac-Man Our proposed ghost team consists of a ghost controlled by rules and three ghosts controlled individually by different MCTS Given a limited time response, in order to increase the reliability of MCTS results, we introduce a mechanism for predicting Ms Pac-Man's future movements and use this mechanism for simulating Ms Pac-Man during Monte Carlo simulations Our ghost team won the first Ms Pac-Man Versus Ghost Team Competition at the 2011 IEEE Congress on Evolutionary Computation (CEC) Its performances for a variety of design choices are also shown and discussedread more
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Journal ArticleDOI
Real-Time Monte Carlo Tree Search in Ms Pac-Man
TL;DR: It is shown that using MCTS is a viable technique for the Pac-Man agent, and the enhancements improve overall performance against four different ghost teams.
Proceedings ArticleDOI
Using genetic programming to evolve heuristics for a Monte Carlo Tree Search Ms Pac-Man agent
Atif M. Alhejali,Simon M. Lucas +1 more
TL;DR: This paper uses Monte Carlo Tree Search to create a Ms Pac-Man playing agent before using genetic programming to enhance its performance by evolving a new default policy to replace the random agent used in the simulations.
Journal ArticleDOI
Algorithms for computing strategies in two-player simultaneous move games
TL;DR: Both novel and existing algorithms that compute strategies for the class of two-player zero-sum simultaneous move games are described, including exact backward induction methods with efficient pruning, as well as Monte Carlo sampling algorithms.
Book ChapterDOI
Fast evolutionary adaptation for Monte Carlo Tree Search
TL;DR: A new adaptive Monte Carlo Tree Search algorithm that uses evolution to rapidly optimise its performance and largely restricts this to modifying the behaviour of the random default policy.
Journal ArticleDOI
Pac-Man Conquers Academia: Two Decades of Research Using a Classic Arcade Game
TL;DR: This paper summarizes the peer-reviewed research that focuses on either Pac-Man (or close variants thereof) with particular emphasis on the field of computational intelligence.
References
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Bandit based monte-carlo planning
Levente Kocsis,Csaba Szepesvári +1 more
TL;DR: In this article, a bandit-based Monte-Carlo planning algorithm is proposed for large state-space Markovian decision problems (MDPs), which is one of the few viable approaches to find near-optimal solutions.
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Efficient selectivity and backup operators in Monte-Carlo tree search
TL;DR: A new framework to combine tree search with Monte-Carlo evaluation, that does not separate between a min-max phase and a Monte- carlo phase is presented, that provides finegrained control of the tree growth, at the level of individual simulations, and allows efficient selectivity.
Proceedings Article
Monte-carlo tree search: a new framework for game AI
TL;DR: This paper puts forward Monte-Carlo Tree Search as a novel, unified framework to game AI, and demonstrates that it can be applied effectively to classic board- games, modern board-games, and video games.