Learning on a Continuum in Evolved Dynamical Node Networks

In artificial life, there has been much previous research using evolution to generate learning behaviour within dynamical system controllers without pre-defining the learning mechanisms; so far this research has focused exclusively on evolving agents that can behave differently in a discrete number of different scenarios, generally two. But many (arguably most) interesting discrimination tasks in real life are where the scenarios are over a continuum; one example would be parental imprinting in birds. Here we analyse a successfully evolved embodied and situated agent on an abstract model of this imprinting and give the first published example of such learning on a continuum.

Izquierdo-Torres, E. and Harvey, I. (2006): Learning on a Continuum in Evolved Dynamical Node Networks. Artificial Life X, Proceedings of the Tenth International Conference on the Simulation and Synthesis of Living Systems, Proceedings Editors: L.M. Rocha, L.S. Yager, M.A. Bedau, D. Floreano, R.L. Goldstone, A. Vespignani, MIT Press. ISBN-10: 0-262-68162-5. Pp. 507-512.


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