Full Text Available
Note: Clicking the button above will open the full text document at the original institutional repository in a new window.
Using particle swarm optimization to evolve two-player game agents
Dissertation (MSc)--University of Pretoria, 2007.
Saved in:
| Other Authors: | |
|---|---|
| Format: | Thesis |
| Published: |
University of Pretoria
2013
|
| Subjects: | |
| Tags: |
No Tags, Be the first to tag this record!
|
| _version_ | 1867613701958270976 |
|---|---|
| access_status_str | Open Access |
| author2 | Fogel, D.B. |
| author_browse | Fogel, D.B. |
| author_facet | Fogel, D.B. |
| collection | Thesis |
| dc_rights_str_mv | © 2006, 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, 2007. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/23980 |
| institution | University of Pretoria (South Africa) |
| last_indexed | 2026-06-10T12:40:20.497Z |
| 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:2263/23980 Using particle swarm optimization to evolve two-player game agents Fogel, D.B. leon.messerschmidt@gmail.com Engelbrecht, Andries P. Messerschmidt, Leon Swarm intelligence Computer games UCTD Dissertation (MSc)--University of Pretoria, 2007. Computer game-playing agents are almost as old as computers themselves, and people have been developing agents since the 1950's. Unfortunately the techniques for game-playing agents have remained basically the same for almost half a century -- an eternity in computer time. Recently developed approaches have shown that it is possible to develop game playing agents with the help of learning algorithms. This study is based on the concept of algorithms that learn how to play board games from zero initial knowledge about playing strategies. A coevolutionary approach, where a neural network is used to assess desirability of leaf nodes in a game tree, and evolutionary algorithms are used to train neural networks in competition, is overviewed. This thesis then presents an alternative approach in which particle swarm optimization (PSO) is used to train the neural networks. Different variations of the PSO are implemented and compared. The results of the PSO approaches are also compared with that of an evolutionary programming approach. The performance of the PSO algorithms is investigated for different values of the PSO control parameters. This study shows that the PSO approach can be applied successfully to train game-playing agents. Computer Science Unrestricted 2013-09-06T16:19:28Z 2007-04-17 2013-09-06T16:19:28Z 2006-05-08 2007-04-17 2007-04-17 Dissertation Messerschmidt, L 2006, Using particle swarm optimization to evolve two-player game agents, MSc(Computer dissertation, University of Pretoria, Pretoria, viewed yymmdd < http://hdl.handle.net/2263/23980 > http://hdl.handle.net/2263/23980 http://upetd.up.ac.za/thesis/available/etd-04172007-083117/ © 2006, 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 | Swarm intelligence Computer games UCTD Using particle swarm optimization to evolve two-player game agents |
| title | Using particle swarm optimization to evolve two-player game agents |
| title_full | Using particle swarm optimization to evolve two-player game agents |
| title_fullStr | Using particle swarm optimization to evolve two-player game agents |
| title_full_unstemmed | Using particle swarm optimization to evolve two-player game agents |
| title_short | Using particle swarm optimization to evolve two-player game agents |
| title_sort | using particle swarm optimization to evolve two player game agents |
| topic | Swarm intelligence Computer games UCTD |
| url | http://hdl.handle.net/2263/23980 http://upetd.up.ac.za/thesis/available/etd-04172007-083117/ |