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Permanent link (DOI): https://doi.org/10.7939/R3908P

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Learning multi-agent pursuit of a moving target Open Access

Descriptions

Other title
Subject/Keyword
moving target search
machine learning
features
Type of item
Thesis
Degree grantor
University of Alberta
Author or creator
Lu, Jieshan
Supervisor and department
Greiner, Russell (Computing Science)
Bulitko, Vadim (Computing Science)
Examining committee member and department
Carbonaro, Michael (Educational Psychology)
Bulitko, Vadim (Computing Science)
Szafron, Duane (Computing Science)
Greiner, Russell (Computing Science)
Department
Department of Computing Science
Specialization

Date accepted
2009-07-21T18:49:26Z
Graduation date
2009-11
Degree
Master of Science
Degree level
Master's
Abstract
In this thesis we consider the task of catching a moving target with multiple pursuers, also known as the “Pursuit Game”, in which coordination among the pursuers is critical. Our testbed is inspired by the pursuit problem in video games, which require fast planning to guarantee fluid frame rates. We apply supervised machine learning methods to automatically derive efficient multi-agent pursuit strategies on rectangular grids. Learning is achieved by computing training data off-line and exploring the game tree on small problems. We also generalize the data to previously unseen and larger problems by learning robust pursuit policies, and run empirical comparisons between several sets of state features using a simple learning architecture. The empirical results show that 1) the application of learning across different maps can help improve game-play performance, especially on non-trivial maps against intelligent targets, and 2) simple heuristic works effectively on simple maps or less intelligent targets.
Language
English
DOI
doi:10.7939/R3908P
Rights
License granted by Jieshan Lu (jieshan@cs.ualberta.ca) on 2009-07-19T18:52:22Z (GMT): Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of the above terms. The author reserves all other publication and other rights in association with the copyright in the thesis, and except as herein provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.
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