Automatic generation of multi-objective neural game controllers using Pareto-based differential evolution
Eversince the born of personal computer to this world, computer games have not been stoping in their development. More and more of computer games are created and more and more genre exsist. RTS games generally are war simulation. The game features of RTS game are very similar to a real war where pl...
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Universiti Malaysia Sabah
2010
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my.ums.eprints.228502019-07-19T06:57:49Z https://eprints.ums.edu.my/id/eprint/22850/ Automatic generation of multi-objective neural game controllers using Pareto-based differential evolution Chin, Kim On Jason Teo Chang, Kee Tong QA Mathematics Eversince the born of personal computer to this world, computer games have not been stoping in their development. More and more of computer games are created and more and more genre exsist. RTS games generally are war simulation. The game features of RTS game are very similar to a real war where player will have to manage their resource income while planning their army, technology, attack location, attack time and much more. Player need to be smart in managing and planning their strategy to overcome their enemies. This also means that RTS game can be a very powerful! testbed for AI. Many researches have done their reseach on improving RTS games such as improving the micro and macro management of the game AI. This report is to investigate how well does Pareto-base Differential Evolution (POE) with a Neural controller perform in a RTS game. A popular billion dollar RTS game, Warcraft III is chosen to be the testbed. For this research, three Evolutionary Algorithm (EA) (Genetic Algorithm (GA), Differential Evolution (DE), and POE) is use to evolve a Feed-Forward Artifitial Neural Networks (FFANN) to playa custom made map in Warcraft III and the outcome is compared amng them. Resutls clearly shows that the hybridized ANN can defenately generate a controller to play the game. DE with FFANN shows a better results comparing to other two approaches. However, PDE with FFANN approach explored a much cheaper of army to win the match. Universiti Malaysia Sabah 2010 Research Report NonPeerReviewed text en https://eprints.ums.edu.my/id/eprint/22850/1/Automatic%20generation%20of%20multi-objective%20neural%20game%20controllers%20using%20Pareto.pdf Chin, Kim On and Jason Teo and Chang, Kee Tong (2010) Automatic generation of multi-objective neural game controllers using Pareto-based differential evolution. (Unpublished) |
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Eversince the born of personal computer to this world, computer games have not been stoping in their development. More and more of computer games are created and more and more genre exsist. RTS games generally are war simulation. The
game features of RTS game are very similar to a real war where player will have to manage their resource income while planning their army, technology, attack location, attack time and much more. Player need to be smart in managing and planning their strategy to overcome their enemies. This also means that RTS game can be a very powerful! testbed for AI. Many researches have done their reseach on improving RTS
games such as improving the micro and macro management of the game AI. This report is to investigate how well does Pareto-base Differential Evolution (POE) with a Neural controller perform in a RTS game. A popular billion dollar RTS game,
Warcraft III is chosen to be the testbed. For this research, three Evolutionary Algorithm (EA) (Genetic Algorithm (GA), Differential Evolution (DE), and POE) is use to evolve a Feed-Forward Artifitial Neural Networks (FFANN) to playa custom made map in Warcraft III and the outcome is compared amng them. Resutls clearly shows that the hybridized ANN can defenately generate a controller to play the game. DE
with FFANN shows a better results comparing to other two approaches. However, PDE with FFANN approach explored a much cheaper of army to win the match. |
format |
Research Report |
author |
Chin, Kim On Jason Teo Chang, Kee Tong |
author_facet |
Chin, Kim On Jason Teo Chang, Kee Tong |
author_sort |
Chin, Kim On |
title |
Automatic generation of multi-objective neural game controllers using Pareto-based differential evolution |
title_short |
Automatic generation of multi-objective neural game controllers using Pareto-based differential evolution |
title_full |
Automatic generation of multi-objective neural game controllers using Pareto-based differential evolution |
title_fullStr |
Automatic generation of multi-objective neural game controllers using Pareto-based differential evolution |
title_full_unstemmed |
Automatic generation of multi-objective neural game controllers using Pareto-based differential evolution |
title_sort |
automatic generation of multi-objective neural game controllers using pareto-based differential evolution |
publisher |
Universiti Malaysia Sabah |
publishDate |
2010 |
url |
https://eprints.ums.edu.my/id/eprint/22850/1/Automatic%20generation%20of%20multi-objective%20neural%20game%20controllers%20using%20Pareto.pdf https://eprints.ums.edu.my/id/eprint/22850/ |
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1760230025138798592 |
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13.18916 |