Discovering several robot behaviors through speciation

Published in Conferences Papers
  1. Leonardo Trujillo, Gustavo Olague, Evelyne Lutton and Francisco Fernández Vega. Behavior-based Speciation for Evolutionary Robotics. In Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation. 2008, 297–298. URL, DOI BibTeX

    @inproceedings{Trujillo:2008:BSE:1389095.1389147,
    	author = "Trujillo, Leonardo and Olague, Gustavo and Lutton, Evelyne and de Vega, Francisco Fern\'{a}ndez",
    	title = "Behavior-based Speciation for Evolutionary Robotics",
    	booktitle = "Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation",
    	series = "GECCO '08",
    	year = 2008,
    	isbn = "978-1-60558-130-9",
    	location = "Atlanta, GA, USA",
    	pages = "297--298",
    	numpages = 2,
    	url = "http://doi.acm.org/10.1145/1389095.1389147",
    	doi = "10.1145/1389095.1389147",
    	acmid = 1389147,
    	publisher = "ACM",
    	address = "New York, NY, USA",
    	keywords = "evolutionary robotics, speciation"
    }
    
Abstract

This contribution studies speciation from the standpoint of evolutionary robotics (ER). A common approach to ER is to design a robot’s control system using neuro-evolution during training. An extension to this methodology is presented here, where speciation is incorporated to the evolution process in order to obtain a varied set of solutions for a robotics problem using a single algorithmic run. Although speciation is common in evolutionary computation, it has been less explored in behavior-based robotics. When employed, speciation usually relies on a distance measure that allows different individuals to be compared. The distance measure is normally computed in objective or phenotypic space. However, the speciation process presented here is intended to produce several distinct robot behaviors; hence, speciation is sought in behavioral space. Thence, individual neurocontrollers are described using behavior signatures, which represent the traversed path of the robot within the training environment and are encoded using a character string. With this representation, behavior signatures are compared using the normalized Levenshtein distance metric (N-GLD). Results indicate that speciation in behavioral space does indeed allow the ER system to obtain several navigation strategies for a common experimental setup. This is illustrated by comparing the best individual from each species with those obtained using the Neuro-Evolution of Augmenting Topologies (NEAT) method which speciates neural networks in topological space.

Published in
4th European Workshop on Bio-Inspired Heuristics for Design Automation (EvoHOT'08)
Volume 4974
Pages 164-174
http://link.springer.com/chapter/10.1007%2F978-3-540-78761-7_17
(Best Paper Award at EvoHOT 2008)
Date of conference
24 - 26 March 2008
ISSN
0302-9743
ISBN
978-3-540-78761-7
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