Software verification of redundancy in neuro-evolutionary robotics

Teo, Jason Tze Wi and Hussein , A. Abbass (2003) Software verification of redundancy in neuro-evolutionary robotics. In: 16th Australian Conference on Artificial Intelligence, 03-05 December 2003, University Western Australia, Perth, Australia.

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Official URL: http://dx.doi.org/10.1007/978-3-540-24581-0_26

Abstract

Evolutionary methods are now commonly used to automatically generate autonomous controllers for physical robots as well as for virtually embodied organisms. Although it is generally accepted that some amount of redundancy may result from using an evolutionary approach, few studies have focused on empirically testing the actual amount of redundancy that is present in controllers generated using artificial evolutionary systems. Network redundancy in certain application domains such as defence, space, and safeguarding, is unacceptable as it puts the reliability of the system at great risk. Thus, our aim in this paper is to test and compare the redundancies of artificial neural network (ANN) controllers that are evolved for a quadrupedal robot using four different evolutionary methodologies. Our results showed that the least amount of redundancy was generated using a self-adaptive Pareto evolutionary multi-objective optimization (EMO) algorithm compared to the more commonly used single-objective evolutionary algorithm (EA) and weighted sum EMO algorithm. Finally, self-adaptation was found to be highly beneficial in reducing redundancy when compared against a hand-tuned Pareto EMO algorithm.

Item Type:Conference Paper (UNSPECIFIED)
Uncontrolled Keywords:Computer science, Artificial intelligence, Robotics, Artificial neural network, Evolutionary multi-objective optimization
Subjects:?? TJ210.2-211.47 ??
Divisions:SCHOOL > School of Engineering and Information Technology
ID Code:589
Deposited By:IR Admin
Deposited On:30 Nov 2011 11:18
Last Modified:10 Sep 2014 14:24

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