Tuci, Elio, Harvey, Inman and Quinn, Matt (2002) Evolving integrated controllers for autonomous learning robots using dynamic neural networks. In: Proceedings of The Seventh International Conference on the Simulation of Adaptive Behavior (SAB'02), 4-9 August 2002, Edinburgh, UK., Edinburgh.

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Abstract

In 1994, Yamauchi and Beer (1994) attempted to evolve a dynamic neural network as a control system for a simulated agent capable of performaning learned behaviour. They tried to evolve an integrated network, i.e. not modularized; this attempt failed. They ended up having to use independent evolution of separate controller modules, arbitrarily partitioned by the researcher. Moreover, they "provided" the agents with hard-wired reinforcement signals. The model we describe in this paper demonstrates that it is possible to evolve an integrated dynamic neural network that successfully controls the behaviour of a khepera robot engaged in a simple learning task. We show that dynamic neural networks, based on leaky-integrator neuron, shaped by evolution, appear to be able to integrate reactive and learned behaviour with an integrated control system which also benefits from its own reinforcement signal.

Item Type:	Conference or Workshop Item (Paper)
Schools and Departments:	School of Engineering and Informatics > Informatics
Depositing User:	Inman Harvey
Date Deposited:	06 Feb 2012 19:52
Last Modified:	13 Apr 2012 10:45
URI:	http://sro.sussex.ac.uk/id/eprint/22734

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