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A learning framework for zero-knowledge game playing agents
Dissertation (MSc)--University of Pretoria, 2007.
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| Format: | Thesis |
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University of Pretoria
2013
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| _version_ | 1867613504923500544 |
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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 | © University of Pretor |
| description | Dissertation (MSc)--University of Pretoria, 2007. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/28767 |
| institution | University of Pretoria (South Africa) |
| last_indexed | 2026-06-10T12:37:12.475Z |
| 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:2263/28767 A learning framework for zero-knowledge game playing agents Engelbrecht, Andries P. wduminy@mweb.co.za Duminy, Willem Harklaas Knowledge discovery Game tree searching. Classification Computational intelligence Machine learning Coevolution Particle swarm optimization (PSO) Checkers Knowledge representation Games UCTD Dissertation (MSc)--University of Pretoria, 2007. The subjects of perfect information games, machine learning and computational intelligence combine in an experiment that investigates a method to build the skill of a game-playing agent from zero game knowledge. The skill of a playing agent is determined by two aspects, the first is the quantity and quality of the knowledge it uses and the second aspect is its search capacity. This thesis introduces a novel representation language that combines symbols and numeric elements to capture game knowledge. Insofar search is concerned; an extension to an existing knowledge-based search method is developed. Empirical tests show an improvement over alpha-beta, especially in learning conditions where the knowledge may be weak. Current machine learning techniques as applied to game agents is reviewed. From these techniques a learning framework is established. The data-mining algorithm, ID3, and the computational intelligence technique, Particle Swarm Optimisation (PSO), form the key learning components of this framework. The classification trees produced by ID3 are subjected to new post-pruning processes specifically defined for the mentioned representation language. Different combinations of these pruning processes are tested and a dominant combination is chosen for use in the learning framework. As an extension to PSO, tournaments are introduced as a relative fitness function. A variety of alternative tournament methods are described and some experiments are conducted to evaluate these. The final design decisions are incorporated into the learning frame-work configuration, and learning experiments are conducted on Checkers and some variations of Checkers. These experiments show that learning has occurred, but also highlights the need for further development and experimentation. Some ideas in this regard conclude the thesis. Computer Science MSc Unrestricted 2013-09-07T14:12:35Z 2007-11-09 2013-09-07T14:12:35Z 2007-04-29 2007-11-09 2007-10-17 Dissertation Duminy, WH 2007-11-09, A learning framework for zero-knowledge game playing agents, MSc Dissertation, University of Pretoria, Pretoria, viewed yymmdd <http://hdl.handle.net/2263/28767> http://hdl.handle.net/2263/28767 http://upetd.up.ac.za/thesis/available/etd-10172007-153836/ © University of Pretor application/pdf University of Pretoria |
| spellingShingle | Knowledge discovery Game tree searching. Classification Computational intelligence Machine learning Coevolution Particle swarm optimization (PSO) Checkers Knowledge representation Games UCTD A learning framework for zero-knowledge game playing agents |
| title | A learning framework for zero-knowledge game playing agents |
| title_full | A learning framework for zero-knowledge game playing agents |
| title_fullStr | A learning framework for zero-knowledge game playing agents |
| title_full_unstemmed | A learning framework for zero-knowledge game playing agents |
| title_short | A learning framework for zero-knowledge game playing agents |
| title_sort | learning framework for zero knowledge game playing agents |
| topic | Knowledge discovery Game tree searching. Classification Computational intelligence Machine learning Coevolution Particle swarm optimization (PSO) Checkers Knowledge representation Games UCTD |
| url | http://hdl.handle.net/2263/28767 http://upetd.up.ac.za/thesis/available/etd-10172007-153836/ |