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...

وصف كامل

محفوظ في:
التفاصيل البيبلوغرافية
المؤلفون الرئيسيون: Othman, Nasri, Decraene, James, Cai, Wentong, Hu, Nan, Low, Malcolm Yoke Hean, Gouaillard, Alexandre
مؤلفون آخرون: School of Computer Engineering
التنسيق: Conference or Workshop Item
اللغة:English
منشور في: 2013
الموضوعات:
الوصول للمادة أونلاين:https://hdl.handle.net/10356/95728
http://hdl.handle.net/10220/11984
الوسوم: إضافة وسم
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المؤسسة: Nanyang Technological University
اللغة: English
الوصف
الملخص: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.