Game AI Generation using Evolutionary Multi-Objective Optimization

被引：0
|
作者：
Tong, Chang Kee [1 ]
On, Chin Kim [1 ]
Teo, Jason [1 ]
Mountstephens, James
机构：
[1] Univ Malaysia Sabah, Evolutionary Comp Lab, Kota Kinabalu, Sabah, Malaysia
来源：
2012 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC) | 2012年
关键词：
Artificial Neural Networks (ANN); Evolutionary Multi-Objective Optimization (EMO); Real-Time Strategy Game (RTS); Pareto Differential Evolution (PDE); Artificial Intelligence (AI); Warcraft III; DIFFERENTIAL EVOLUTION;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
This paper presents the design and evaluation of a full AI controller for Real-Time Strategy (RTS) games using techniques from Evolutionary Computing (EC). The design is novel in its use of a modified Pareto Differential Evolution (PDE) algorithm for bi-objective optimization of the weights of an Artificial Neural Network (ANN) controller when only single-objective optimization has so far been studied. The two main aims of this research are to: (1) develop controllers capable of defeating opponents of varying difficulty levels, which may assist in commercial RTS AI development, and (2) minimize the number of neurons used in the ANN architecture, an issue primarily of efficiency. Experimental results using the popular Warcraft III platform demonstrate success with both aims: the optimized controller was able to win any battle using only a minimal number of hidden neurons, but sub-optimal controllers were able to provide opponents of any intermediate difficulty.
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页数：8
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