Adaptive networks for robotics and the emergence of reward anticipatory circuits

McHale, Gary (2012) Adaptive networks for robotics and the emergence of reward anticipatory circuits. Doctoral thesis (PhD), University of Sussex.

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Abstract

Currently the central challenge facing evolutionary robotics is to determine
how best to extend the range and complexity of behaviour supported by evolved
neural systems. Implicit in the work described in this thesis is the idea that this
might best be achieved through devising neural circuits (tractable to evolutionary
exploration) that exhibit complementary functional characteristics. We concentrate
on two problem domains; locomotion and sequence learning. For locomotion
we compare the use of GasNets and other adaptive networks. For sequence learning
we introduce a novel connectionist model inspired by the role of dopamine
in the basal ganglia (commonly interpreted as a form of reinforcement learning).
This connectionist approach relies upon a new neuron model inspired by notions
of energy efficient signalling. Two reward adaptive circuit variants were investigated.
These were applied respectively to two learning problems; where action
sequences are required to take place in a strict order, and secondly, where action
sequences are robust to intermediate arbitrary states. We conclude the thesis
by proposing a formal model of functional integration, encompassing locomotion
and sequence learning, extending ideas proposed by W. Ross Ashby.
A general model of the adaptive replicator is presented, incoporating subsystems
that are tuned to continuous variation and discrete or conditional events.
Comparisons are made with Ross W. Ashby's model of ultrastability and his
ideas on adaptive behaviour. This model is intended to support our assertion
that, GasNets (and similar networks) and reward adaptive circuits of the type
presented here, are intrinsically complementary. In conclusion we present some
ideas on how the co-evolution of GasNet and reward adaptive circuits might lead
us to significant improvements in the synthesis of agents capable of exhibiting
complex adaptive behaviour.

Item Type: Thesis (Doctoral)
Schools and Departments: School of Engineering and Informatics > Informatics
Subjects: T Technology > TJ Mechanical engineering and machinery > TJ0210.2 Mechanical devices and figures. Automata. Ingenious mechanisms. Robots (General)
Depositing User: Library Cataloguing
Date Deposited: 27 Nov 2012 07:53
Last Modified: 07 Sep 2015 15:04
URI: http://sro.sussex.ac.uk/id/eprint/42409

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