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A coevolutionary multiobjective evolutionary algorithm for game artificial intelligence

Recently, the growth of Artificial Intelligence (AI) has provided a set of effective techniques for designing computer-based controllers to perform various tasks autonomously in game area, specifically to produce intelligent optimal game controllers for playing video and computer games. This paper...

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Main Authors: Tse, Guan Tan, Jason, Teo, Kim, On Chin, Alfred, Rayner
Format: Article
Language:English
Published: Penerbit Universiti Kebangsaan Malaysia 2013
Online Access:http://journalarticle.ukm.my/6646/1/4297-9967-1-PB.pdf
http://journalarticle.ukm.my/6646/
http://ejournals.ukm.my/apjitm/index
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spelling my-ukm.journal.66462016-12-14T06:41:48Z http://journalarticle.ukm.my/6646/ A coevolutionary multiobjective evolutionary algorithm for game artificial intelligence Tse, Guan Tan Jason, Teo Kim, On Chin Alfred, Rayner Recently, the growth of Artificial Intelligence (AI) has provided a set of effective techniques for designing computer-based controllers to perform various tasks autonomously in game area, specifically to produce intelligent optimal game controllers for playing video and computer games. This paper explores the use of the competitive fitness strategy: K Random Opponents (KRO) in a multiobjective approach for evolving Artificial Neural Networks (ANNs) that act as controllers for the Ms. Pac-man agent. The Pareto Archived Evolution Strategy (PAES) algorithm is used to generate a Pareto optimal set of ANNs that optimize the conflicting objectives of maximizing game scores and minimizing neural network complexity. Furthermore, an improved version, namely PAESNet_KRO, is proposed, which incorporates in contrast to its predecessor KRO strategy. The results are compared with PAESNet. From the discussions, it is found that PAESNet_KRO provides better solutions than PAESNet. The PAESNet_KRO can evolve a set of nondominated solutions that cover the solutions of PAESNet. Penerbit Universiti Kebangsaan Malaysia 2013-12 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/6646/1/4297-9967-1-PB.pdf Tse, Guan Tan and Jason, Teo and Kim, On Chin and Alfred, Rayner (2013) A coevolutionary multiobjective evolutionary algorithm for game artificial intelligence. Asia-Pacific Journal of Information Technology and Multimedia, 2 (2). pp. 53-61. ISSN 2289-2192 http://ejournals.ukm.my/apjitm/index
institution Universiti Kebangsaan Malaysia
building Perpustakaan Tun Sri Lanang Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Kebangsaan Malaysia
content_source UKM Journal Article Repository
url_provider http://journalarticle.ukm.my/
language English
description Recently, the growth of Artificial Intelligence (AI) has provided a set of effective techniques for designing computer-based controllers to perform various tasks autonomously in game area, specifically to produce intelligent optimal game controllers for playing video and computer games. This paper explores the use of the competitive fitness strategy: K Random Opponents (KRO) in a multiobjective approach for evolving Artificial Neural Networks (ANNs) that act as controllers for the Ms. Pac-man agent. The Pareto Archived Evolution Strategy (PAES) algorithm is used to generate a Pareto optimal set of ANNs that optimize the conflicting objectives of maximizing game scores and minimizing neural network complexity. Furthermore, an improved version, namely PAESNet_KRO, is proposed, which incorporates in contrast to its predecessor KRO strategy. The results are compared with PAESNet. From the discussions, it is found that PAESNet_KRO provides better solutions than PAESNet. The PAESNet_KRO can evolve a set of nondominated solutions that cover the solutions of PAESNet.
format Article
author Tse, Guan Tan
Jason, Teo
Kim, On Chin
Alfred, Rayner
spellingShingle Tse, Guan Tan
Jason, Teo
Kim, On Chin
Alfred, Rayner
A coevolutionary multiobjective evolutionary algorithm for game artificial intelligence
author_facet Tse, Guan Tan
Jason, Teo
Kim, On Chin
Alfred, Rayner
author_sort Tse, Guan Tan
title A coevolutionary multiobjective evolutionary algorithm for game artificial intelligence
title_short A coevolutionary multiobjective evolutionary algorithm for game artificial intelligence
title_full A coevolutionary multiobjective evolutionary algorithm for game artificial intelligence
title_fullStr A coevolutionary multiobjective evolutionary algorithm for game artificial intelligence
title_full_unstemmed A coevolutionary multiobjective evolutionary algorithm for game artificial intelligence
title_sort coevolutionary multiobjective evolutionary algorithm for game artificial intelligence
publisher Penerbit Universiti Kebangsaan Malaysia
publishDate 2013
url http://journalarticle.ukm.my/6646/1/4297-9967-1-PB.pdf
http://journalarticle.ukm.my/6646/
http://ejournals.ukm.my/apjitm/index
_version_ 1643736844911771648
score 13.211869

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