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PSO-based coevolutionary Game Learning
Dissertation (MSc)--University of Pretoria, 2005.
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| Format: | Thesis |
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University of Pretoria
2013
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| _version_ | 1867613671773962240 |
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| access_status_str | Open Access |
| author2 | Engelbrecht, Andries P. |
| author_browse | Engelbrecht, Andries P. |
| author_facet | Engelbrecht, Andries P. |
| collection | Thesis |
| dc_rights_str_mv | © 2004, University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. |
| description | Dissertation (MSc)--University of Pretoria, 2005. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/30166 |
| institution | University of Pretoria (South Africa) |
| last_indexed | 2026-06-10T12:39:51.634Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository |
| publishDate | 2013 |
| publishDateRange | 2013 |
| publishDateSort | 2013 |
| publisher | University of Pretoria |
| publisherStr | University of Pretoria |
| record_format | dspace |
| source_str | UPSpace — University of Pretoria Institutional Repository |
| spelling | oai:repository.up.ac.za:2263/30166 PSO-based coevolutionary Game Learning Engelbrecht, Andries P. nfranken@cs.up.ac.za Franken, Cornelis J. Games Machine learning Neural networks Particle swarm optimization (PSO) Iterated prisoner’s dilemma Evolutionary computation Coevolution Checkers Computational intelligence UCTD Dissertation (MSc)--University of Pretoria, 2005. Games have been investigated as computationally complex problems since the inception of artificial intelligence in the 1950’s. Originally, search-based techniques were applied to create a competent (and sometimes even expert) game player. The search-based techniques, such as game trees, made use of human-defined knowledge to evaluate the current game state and recommend the best move to make next. Recent research has shown that neural networks can be evolved as game state evaluators, thereby removing the human intelligence factor completely. This study builds on the initial research that made use of evolutionary programming to evolve neural networks in the game learning domain. Particle Swarm Optimisation (PSO) is applied inside a coevolutionary training environment to evolve the weights of the neural network. The training technique is applied to both the zero sum and non-zero sum game domains, with specific application to Tic-Tac-Toe, Checkers and the Iterated Prisoners Dilemma (IPD). The influence of the various PSO parameters on playing performance are experimentally examined, and the overall performance of three different neighbourhood information sharing structures compared. A new coevolutionary scoring scheme and particle dispersement operator are defined, inspired by Formula One Grand Prix racing. Finally, the PSO is applied in three novel ways to evolve strategies for the IPD – the first application of its kind in the PSO field. The PSO-based coevolutionary learning technique described and examined in this study shows promise in evolving intelligent evaluators for the aforementioned games, and further study will be conducted to analyse its scalability to larger search spaces and games of varying complexity. Computer Science unrestricted 2013-09-07T18:10:06Z 2004-12-07 2013-09-07T18:10:06Z 2004-05-08 2005-12-07 2004-12-07 Dissertation Franken, C 2004, PSO-based coevolutionary Game Learning, MSc dissertation, University of Pretoria, Pretoria, viewed yymmdd < http://hdl.handle.net/2263/30166 > http://hdl.handle.net/2263/30166 http://upetd.up.ac.za/thesis/available/etd-12072004-074439/ © 2004, University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. application/pdf University of Pretoria |
| spellingShingle | Games Machine learning Neural networks Particle swarm optimization (PSO) Iterated prisoner’s dilemma Evolutionary computation Coevolution Checkers Computational intelligence UCTD PSO-based coevolutionary Game Learning |
| title | PSO-based coevolutionary Game Learning |
| title_full | PSO-based coevolutionary Game Learning |
| title_fullStr | PSO-based coevolutionary Game Learning |
| title_full_unstemmed | PSO-based coevolutionary Game Learning |
| title_short | PSO-based coevolutionary Game Learning |
| title_sort | pso based coevolutionary game learning |
| topic | Games Machine learning Neural networks Particle swarm optimization (PSO) Iterated prisoner’s dilemma Evolutionary computation Coevolution Checkers Computational intelligence UCTD |
| url | http://hdl.handle.net/2263/30166 http://upetd.up.ac.za/thesis/available/etd-12072004-074439/ |