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Coevolution of Neuro-controllers to Train Multi-Agent Teams from Zero Knowledge
Dissertation (MSc)--University of Pretoria, 2013.
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| Format: | Thesis |
| Language: | Eng |
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University of Pretoria
2013
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| _version_ | 1867613579381833728 |
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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 | © 2013 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, 2013. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/31625 |
| institution | University of Pretoria (South Africa) |
| language | Eng |
| last_indexed | 2026-06-10T12:38:23.596Z |
| 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:2263/31625 Coevolution of Neuro-controllers to Train Multi-Agent Teams from Zero Knowledge Engelbrecht, Andries P. Scheepers, Christiaan Multi agent system Cooperative coevolution Simple soccer Zero knowledge Competitive coevolution Neural networks Charged particle swarm optimiser UCTD Dissertation (MSc)--University of Pretoria, 2013. After the historic chess match between Deep Blue and Garry Kasparov, many researchers considered the game of chess solved and moved on to the more complex game of soccer. Artificial intelligence research has shifted focus to creating artificial players capable of mimicking the task of playing soccer. A new training algorithm is presented in this thesis for training teams of players from zero knowledge, evaluated on a simplified version of the game of soccer. The new algorithm makes use of the charged particle swarm optimiser as a neural network trainer in a coevolutionary training environment. To counter the lack of domain information a new relative fitness measure based on the FIFA league-ranking system was developed. The function provides a granular relative performance measure for competitive training. Gameplay strategies that resulted from the trained players are evaluated. It was found that the algorithm successfully trains teams of agents to play in a cooperative manner. Techniques developed in this study may also be widely applied to various other artificial intelligence fields. Computer Science unrestricted 2013-09-10T07:02:00Z 2013 2013-09-10T07:02:00Z 2013-07-25 2013 2013-07-25 Dissertation Scheepers, C. 2013, Coevolution of Neuro-controllers to Train Multi-Agent Teams from Zero Knowledge, MSc dissertation, University of Pretoria, Pretoria, viewed yymmdd <http://hdl.handle.net/2263/31625> C13/9/1004 http://hdl.handle.net/2263/31625 Eng © 2013 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 | Multi agent system Cooperative coevolution Simple soccer Zero knowledge Competitive coevolution Neural networks Charged particle swarm optimiser UCTD Coevolution of Neuro-controllers to Train Multi-Agent Teams from Zero Knowledge |
| title | Coevolution of Neuro-controllers to Train Multi-Agent Teams from Zero Knowledge |
| title_full | Coevolution of Neuro-controllers to Train Multi-Agent Teams from Zero Knowledge |
| title_fullStr | Coevolution of Neuro-controllers to Train Multi-Agent Teams from Zero Knowledge |
| title_full_unstemmed | Coevolution of Neuro-controllers to Train Multi-Agent Teams from Zero Knowledge |
| title_short | Coevolution of Neuro-controllers to Train Multi-Agent Teams from Zero Knowledge |
| title_sort | coevolution of neuro controllers to train multi agent teams from zero knowledge |
| topic | Multi agent system Cooperative coevolution Simple soccer Zero knowledge Competitive coevolution Neural networks Charged particle swarm optimiser UCTD |
| url | http://hdl.handle.net/2263/31625 |