NDLI: Uniform versus Gaussian mutators in automatic generation of game AI in Ms. Pac-man using hill-climbing

Content Provider	IEEE Xplore Digital Library
Author	Tse Guan Tan Teo, J. Anthony, P.
Copyright Year	2010
Description	Author affiliation: Center of Excellence in Semantic Agents Universiti Malaysia Sabah (Anthony, P.) \|\| Evolutionary Computing Laboratory Universiti Malaysia Sabah (Tse Guan Tan; Teo, J.)
Abstract	This paper explores the idea of combining the hill-climbing concept into feed-forward artificial neural networks (ANN) to develop intelligent controllers to play the Ms. Pacman game. The resulting algorithm is referred to as the HillClimbingNet. A comparison with a random system, called RandNet is conducted on the same problem. We also present a survey of the effects of two most popular probability density functions, uniform and Gaussian distributions/mutators on the introduced algorithm. The results clearly indicate the strong potential of the hill-climbing strategy as a direct search method in tandem with a Gaussian-based mutator to optimize the ANN for playing Ms. Pac-Man.
Starting Page	282
Ending Page	286
File Size	1642967
Page Count	5
File Format	PDF
ISBN	9781424456505
DOI	10.1109/INFRKM.2010.5466903
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-03-17
Publisher Place	Malaysia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Optimization methods Artificial neural networks Gaussian distribution Intelligent networks Search methods Gaussian processes Feed-forward artificial neural networks Automatic control Probability density function Gaussian-based mutator Hill-climbing Artificial intelligence Feedforward systems Ms. Pac-man
Content Type	Text
Resource Type	Article

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