Simulation-based optimization of StarCraft tactical AI through evolutionary computation
The development of competent AI for real-time strategy games such as StarCraft is made difficult by the myriad of strategic and tactical reasonings which must be performed concurrently. A significant portion of StarCraft gameplay is in managing tactical conflict with opposing forces. We present a mo...
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sg-ntu-dr.10356-957282020-05-28T07:19:06Z Simulation-based optimization of StarCraft tactical AI through evolutionary computation Othman, Nasri Decraene, James Cai, Wentong Hu, Nan Low, Malcolm Yoke Hean Gouaillard, Alexandre School of Computer Engineering IEEE Conference on Computational Intelligence and Games (2012 : Granada, Spain) DRNTU::Engineering::Computer science and engineering The development of competent AI for real-time strategy games such as StarCraft is made difficult by the myriad of strategic and tactical reasonings which must be performed concurrently. A significant portion of StarCraft gameplay is in managing tactical conflict with opposing forces. We present a modular framework for simulating AI vs. AI conflicts through an XML specification, whereby the behavioural and tactical components for each force can be varied. Evolutionary computation can be employed on aspects of the scenario to yield superior solutions. Through evolution, a StarCraft AI tournament bot achieved a success rate of 68% against its original version. We also demonstrate the use of evolutionary computation to yield a tactical attack path to maximise enemy casualties. We believe that our framework can be used to perform automatic refinement on AI bots in StarCraft. 2013-07-22T06:19:27Z 2019-12-06T19:20:25Z 2013-07-22T06:19:27Z 2019-12-06T19:20:25Z 2012 2012 Conference Paper Othman, N., Decraene, J., Cai, W., Hu, N., Low, M. Y. H., & Gouaillard, A. (2012). Simulation-based optimization of StarCraft tactical AI through evolutionary computation. 2012 IEEE Conference on Computational Intelligence and Games (CIG). https://hdl.handle.net/10356/95728 http://hdl.handle.net/10220/11984 10.1109/CIG.2012.6374182 en © 2012 IEEE. |
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DRNTU::Engineering::Computer science and engineering Othman, Nasri Decraene, James Cai, Wentong Hu, Nan Low, Malcolm Yoke Hean Gouaillard, Alexandre Simulation-based optimization of StarCraft tactical AI through evolutionary computation |
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The development of competent AI for real-time strategy games such as StarCraft is made difficult by the myriad of strategic and tactical reasonings which must be performed concurrently. A significant portion of StarCraft gameplay is in managing tactical conflict with opposing forces. We present a modular framework for simulating AI vs. AI conflicts through an XML specification, whereby the behavioural and tactical components for each force can be varied. Evolutionary computation can be employed on aspects of the scenario to yield superior solutions. Through evolution, a StarCraft AI tournament bot achieved a success rate of 68% against its original version. We also demonstrate the use of evolutionary computation to yield a tactical attack path to maximise enemy casualties. We believe that our framework can be used to perform automatic refinement on AI bots in StarCraft. |
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School of Computer Engineering |
author_facet |
School of Computer Engineering Othman, Nasri Decraene, James Cai, Wentong Hu, Nan Low, Malcolm Yoke Hean Gouaillard, Alexandre |
format |
Conference or Workshop Item |
author |
Othman, Nasri Decraene, James Cai, Wentong Hu, Nan Low, Malcolm Yoke Hean Gouaillard, Alexandre |
author_sort |
Othman, Nasri |
title |
Simulation-based optimization of StarCraft tactical AI through evolutionary computation |
title_short |
Simulation-based optimization of StarCraft tactical AI through evolutionary computation |
title_full |
Simulation-based optimization of StarCraft tactical AI through evolutionary computation |
title_fullStr |
Simulation-based optimization of StarCraft tactical AI through evolutionary computation |
title_full_unstemmed |
Simulation-based optimization of StarCraft tactical AI through evolutionary computation |
title_sort |
simulation-based optimization of starcraft tactical ai through evolutionary computation |
publishDate |
2013 |
url |
https://hdl.handle.net/10356/95728 http://hdl.handle.net/10220/11984 |
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1681058591864258560 |