NDLI: Pareto Evolution and Co-Evolution in Cognitive Neural Agents Synthesis for Tic-Tac-Toe

Content Provider	IEEE Xplore Digital Library
Author	Yi Jack Yau Teo, J. Anthony, P.
Copyright Year	2007
Description	Author affiliation: Sch. of Eng. & Inf. Technol., Universiti Malaysia Sabah, Kota Kinabalu (Yi Jack Yau; Teo, J.; Anthony, P.)
Abstract	Although a number of multi-objective evolutionary algorithms (MOEAs) have been proposed over the last two decades, very few studies have utilized MOEAs for game agent synthesis. Recently, we have suggested a co-evolutionary implementation using the Pareto evolutionary programming (PEP) algorithm. This paper describes a series of experiments using PEP for evolving artificial neural networks (ANNs) that act as game-playing agents. Three systems are compared: (i) a canonical PEP system, (ii) a co-evolving PEP system (PCEP) with 3 different setups, and (iii) a co-evolving PEP system that uses an archive (PCEP-A) with 3 different setups. The aim of this study is to provide insights on the effects of including co-evolutionary techniques on a 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 PEP system outperformed both co-evolutionary PEP 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
Starting Page	304
Ending Page	311
File Size	145976
Page Count	8
File Format	PDF
ISBN	1424407095
DOI	10.1109/CIG.2007.368113
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-04-01
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Automatic programming Evolutionary computation Artificial neural networks Pareto optimization Co-Evolution Game theory Pareto Differential Evolution Evolution (biology) Evolutionary Multi-Objective Optimization Neural networks Genetic programming Game AI Network synthesis Artificial intelligence Evolutionary Artificial Neural Networks
Content Type	Text
Resource Type	Article

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