Yi , Jack Yau and Teo, Jason Tze Wi and Patricia Anthony, (2007) Pareto evolution and co-evolution in cognitive game AI synthesis. In: 4th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2007, 5-8 March 2007 , Matsushima, Japan.
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Official URL: http://dx.doi.org/10.1007/978-3-540-70928-2_20
The Pareto-based Differential Evolution (PDE) algorithm is one of the current state-of-the-art Multi-objective Evolutionary Algorithms (MOEAs). This paper describes a series of experiments using PDE for evolving artificial neural networks (ANNs) that act as game-playing agents. Three systems are compared: (i) a canonical PDE system, (ii) a co-evolving PDE system (PCDE) with 3 different setups, and (iii) a co-evolving PDE system that uses an archive (PCDE-A) with 3 different setups. The aim of this study is to provide insights on the effects of including co-evolutionary techniques on a well-known MOEA by investigating and comparing these 3 different approaches in evolving intelligent agents as both first and second players in a deterministic zero-sum board game. The results indicate that the canonical PDE system outperformed both co-evolutionary PDE systems as it was able to evolve ANN agents with higher quality game-playing performance as both first and second game players. Hence, this study shows that a canonical MOEA without co-evolution is desirable for the synthesis of cognitive game AI agents. © Springer-Verlag Berlin Heidelberg 2007.
|Item Type:||Conference Paper (UNSPECIFIED)|
|Uncontrolled Keywords:||Co-evolution, Evolutionary artificial neural networks, Evolutionary multi-objective optimization, Game AI, Pareto differential evolution|
|Subjects:||?? QA75-76.95 ??|
|Divisions:||SCHOOL > School of Engineering and Information Technology|
|Deposited By:||IR Admin|
|Deposited On:||19 May 2011 16:12|
|Last Modified:||29 Dec 2014 16:35|
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