Evolving robot sub-behaviour modules using Gene Expression Programming

dc.contributor.authorMwaura, Jonathan
dc.contributor.authorKeedwell, Ed
dc.date.accessioned2014-08-12T05:32:30Z
dc.date.available2014-08-12T05:32:30Z
dc.date.issued2015-05
dc.description.abstractMany approaches to AI in robotics use a multi-layered approach to determine levels of behaviour from basic operations to goal-directed behaviour, the most well-known of which is the subsumption architecture. In this paper, the performances of the unigenic gene expression programming (ugGEP) and multigenic GEP (mgGEP) in evolving robot controllers for a wall following robot is analysed. Additionally, the paper introduces Regulatory Multigenic Gene Expression Programming (RMGEP), a new evolutionary technique that can be utilised to automatically evolve modularity in robot behaviour. The proposed technique extends the mgGEP algorithm, by incorporating a regulatory gene as part of the GEP chromosome. The regulatory gene, just as in systems biology, determines which of the genes in the chromosome to express and therefore how the controller solves the problem. In the initial experiments, the proposed algorithm is implemented for a robot wall following problem and the results compared to that of ugGEP and mgGEP. In addition to the wall following behaviour, a robot foraging behaviour is implemented with the aim of investigating whether the position of a speci c module (sub-expression tree (ET)) in the overall ET is of importance when coding for a problem.en_US
dc.description.embargo2016-05-30
dc.description.librarianhb2014en_US
dc.description.urihttp://link.springer.com/journal/10710en_US
dc.identifier.citationMwaura, J & Keedwell, E 2015, 'Evolving robot sub-behaviour modules using Gene Expression Programming', Genetic Programming and Evolvable Machines, vol. 16, no. 2, pp. 95-131.en_US
dc.identifier.issn1389-2576 (print)
dc.identifier.issn1573-7632 (online)
dc.identifier.other10.1007/s10710-014-9229-x
dc.identifier.urihttp://hdl.handle.net/2263/41162
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.rights© Springer Science+Business Media New York 2014. The original publication is available at : http://link.springer.com/journal/10710.en_US
dc.subjectSubsumption architectureen_US
dc.subjectLayered learningen_US
dc.subjectEvolutionary roboticsen_US
dc.subjectRobot behaviour coordinationen_US
dc.subjectGene Expression Programming (GEP)en_US
dc.titleEvolving robot sub-behaviour modules using Gene Expression Programmingen_US
dc.typePostprint Articleen_US

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