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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Bibliographic Details
Main Authors: Vishnu Kotrajaras, Tanawat Kumnoonsate
Format: Article
Language:English
Published: Hindawi Limited 2009-01-01
Series:International Journal of Computer Games Technology
Online Access:http://dx.doi.org/10.1155/2009/436732
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spelling 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
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