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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Main Authors: Othman, Nasri, Decraene, James, Cai, Wentong, Hu, Nan, Low, Malcolm Yoke Hean, Gouaillard, Alexandre
Other Authors: School of Computer Engineering
Format: Conference or Workshop Item
Language:English
Published: 2013
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Online Access:https://hdl.handle.net/10356/95728
http://hdl.handle.net/10220/11984
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Institution: Nanyang Technological University
Language: English
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spelling 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.
institution Nanyang Technological University
building NTU Library
country Singapore
collection DR-NTU
language English
topic DRNTU::Engineering::Computer science and engineering
spellingShingle 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
description 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.
author2 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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