CoEvolutionary Incremental Modelling of Robotic Cognitive Mechanisms

Recently, brain models attempt to support cognitive abilities of artificial organisms. Incremental approaches are often employed to support modelling process. The present work introduces a novel computational framework for incremental brain modelling, which aims at enforcing partial components re-usability. A coevolutionary agent-based approach is followed which utilizes properly formulated neural agents to represent brain areas. A collaborative coevolutionary method, with the inherent ability to design cooperative substructures, supports the implementation of partial brain models, and additionally supplies a consistent method to achieve their integration. The implemented models are embedded in a robotic platform to support its behavioral capabilities.

[1]  M. Alexander,et al.  Principles of Neural Science , 1981 .

[2]  Panos E. Trahanias,et al.  Modelling brain emergent behaviours through coevolution of neural agents , 2006, Neural Networks.

[3]  Brian Scassellati,et al.  Theory of Mind for a Humanoid Robot , 2002, Auton. Robots.

[4]  P. Goldman-Rakic,et al.  Synaptic mechanisms and network dynamics underlying spatial working memory in a cortical network model. , 2000, Cerebral cortex.

[5]  Riccardo Poli,et al.  Genetic and Evolutionary Computation – GECCO 2004 , 2004, Lecture Notes in Computer Science.

[6]  G. Sandini,et al.  Babybot : an artificial developing robotic agent , 2000 .

[7]  Joaquín M. Fuster,et al.  Executive frontal functions , 2000, Experimental Brain Research.

[8]  E. Rolls,et al.  On the design of neural networks in the brain by genetic evolution , 2000, Progress in Neurobiology.

[9]  Panos E. Trahanias,et al.  Evolution Tunes Coevolution: Modelling Robot Cognition Mechanisms , 2004, GECCO.

[10]  M. Maniadakis,et al.  A hierarchical coevolutionary method to support brain-lesion modelling , 2005, Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005..

[11]  Giulio Sandini,et al.  Babybot: a biologically inspired developing robotic agent , 2000, SPIE Optics East.

[12]  Dario Floreano,et al.  Evolutionary robots with on-line self-organization and behavioral fitness , 2000, Neural Networks.

[13]  Giorgio Valentini,et al.  Ensembles of Learning Machines , 2002, WIRN.

[14]  R. Cotterill.  Cooperation of the basal ganglia, cerebellum, sensory cerebrum and hippocampus: possible implications for cognition, consciousness, intelligence and creativity , 2001, Progress in Neurobiology.

[15]  R. Kesner,et al.  The role of rat dorsomedial prefrontal cortex in working memory for egocentric responses , 2001, Neuroscience Letters.

[16]  Risto Miikkulainen,et al.  Forming Neural Networks Through Efficient and Adaptive Coevolution , 1997, Evolutionary Computation.

[17]  Ronan G. Reilly,et al.  Cortical software re-use: a computational principle for cognitive development in robots , 2002, Proceedings of the 9th International Conference on Neural Information Processing, 2002. ICONIP '02..

[18]  Kenneth A. De Jong,et al.  Cooperative Coevolution: An Architecture for Evolving Coadapted Subcomponents , 2000, Evolutionary Computation.