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Title: Methods for tackling games of strict competition
Author: Samothrakis, Spyridon
ISNI:       0000 0004 5358 3975
Awarding Body: University of Essex
Current Institution: University of Essex
Date of Award: 2014
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The primary goal of this thesis is to develop algorithms that can approximately but robustly solve strictly competitive games. Two streams of research are explored, which also form the main contributions of this thesis. The first one involves transferring techniques used in combinatorial games to real-time video games, allowing for strong players that can take decisions fast. The second one involves using evolutionary computation to approximate solutions in an off-line fashion in games of both perfect and imperfect information. The algorithms proposed are presented in this thesis alongside a number of experiments, which involve two real-time games (Tron and Pacman), a strategy board game (Othello) and a game of imperfect information (2-Player Texas Holdem). The experiments cover a wide range of game scenarios, each aimed at uncovering different facets of the algorithms used. For real time games we conclude that strong a priori (or habitual) knowledge is required in order to act fast and successfully, but a player can massively benefit if this knowledge is combined with strong forward model exploitation methods like Monte-Carlo Tree Search. We show that Evolutionary Algorithms can be successfully used to obtain such a priori knowledge. Finally, for games of imperfect information, we show that one is able to obtain strong players offline using a novel iterative method, however limitations in the function approximation schemes used mean that these methods are not optimal.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available