Fine-Tuning Parameters for Emergent Environments in Games Using Artificial Intelligence
This paper presents the design, development, and test results of a tool for adjusting properties of emergent environment maps automatically according to a given scenario. Adjusting properties for a scenario allows a specific scene to take place while still enables players to meddle with emergent map...
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2009-01-01
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Series: | International Journal of Computer Games Technology |
Online Access: | http://dx.doi.org/10.1155/2009/436732 |
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doaj-5253bad9abfa4a15ac8e96d109b804502020-11-24T23:24:24ZengHindawi LimitedInternational Journal of Computer Games Technology1687-70471687-70552009-01-01200910.1155/2009/436732436732Fine-Tuning Parameters for Emergent Environments in Games Using Artificial IntelligenceVishnu Kotrajaras0Tanawat Kumnoonsate1Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University, Payathai Road, Patumwan Bangkok 10330, ThailandDepartment of Computer Engineering, Faculty of Engineering, Chulalongkorn University, Payathai Road, Patumwan Bangkok 10330, ThailandThis paper presents the design, development, and test results of a tool for adjusting properties of emergent environment maps automatically according to a given scenario. Adjusting properties for a scenario allows a specific scene to take place while still enables players to meddle with emergent maps. The tool uses genetic algorithm and steepest ascent hill-climbing to learn and adjust map properties.Using the proposed tool, the need for time-consuming and labor-intensive parameter adjustments when setting up scenarios in emergent environment maps is greatly reduced. The tool works by converting the paths of events created by users (i.e., the spreading of fire and the flow of water) for a map to the properties of the map that plays out the scenario set by the given paths of events. Vital event points are preserved while event points outside the given scenario are minimized. Test results show that the tool preserves more than 70 percent of vital event points and reduces event points outside given scenarios to less than 3 percent.http://dx.doi.org/10.1155/2009/436732 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Vishnu Kotrajaras Tanawat Kumnoonsate |
spellingShingle |
Vishnu Kotrajaras Tanawat Kumnoonsate Fine-Tuning Parameters for Emergent Environments in Games Using Artificial Intelligence International Journal of Computer Games Technology |
author_facet |
Vishnu Kotrajaras Tanawat Kumnoonsate |
author_sort |
Vishnu Kotrajaras |
title |
Fine-Tuning Parameters for Emergent Environments in Games Using Artificial Intelligence |
title_short |
Fine-Tuning Parameters for Emergent Environments in Games Using Artificial Intelligence |
title_full |
Fine-Tuning Parameters for Emergent Environments in Games Using Artificial Intelligence |
title_fullStr |
Fine-Tuning Parameters for Emergent Environments in Games Using Artificial Intelligence |
title_full_unstemmed |
Fine-Tuning Parameters for Emergent Environments in Games Using Artificial Intelligence |
title_sort |
fine-tuning parameters for emergent environments in games using artificial intelligence |
publisher |
Hindawi Limited |
series |
International Journal of Computer Games Technology |
issn |
1687-7047 1687-7055 |
publishDate |
2009-01-01 |
description |
This paper presents the design, development, and test results of a tool for adjusting properties of emergent environment maps automatically according to a given scenario. Adjusting properties for a scenario allows a specific scene to take place while still enables players to meddle with emergent maps. The tool uses genetic algorithm and steepest ascent hill-climbing to learn and adjust map properties.Using the proposed tool, the need for time-consuming and labor-intensive parameter adjustments when setting up scenarios in emergent environment maps is greatly reduced. The tool works by converting the paths of events created by users (i.e., the spreading of fire and the flow of water) for a map to the properties of the map that plays out the scenario set by the given paths of events. Vital event points are preserved while event points outside the given scenario are minimized. Test results show that the tool preserves more than 70 percent of vital event points and reduces event points outside given scenarios to less than 3 percent. |
url |
http://dx.doi.org/10.1155/2009/436732 |
work_keys_str_mv |
AT vishnukotrajaras finetuningparametersforemergentenvironmentsingamesusingartificialintelligence AT tanawatkumnoonsate finetuningparametersforemergentenvironmentsingamesusingartificialintelligence |
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