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Evolving integrated controllers for autonomous learning robots using dynamic neural networks.
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posted on 2023-06-08, 00:23 authored by Elio Tuci, Inman HarveyInman Harvey, Matt QuinnIn 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.
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Publication status
- Published
Publisher
MIT PressPages
10.0Presentation Type
- paper
Event name
Proceedings of The Seventh International Conference on the Simulation of Adaptive Behavior (SAB'02), 4-9 August 2002, Edinburgh, UK.Event location
EdinburghEvent type
conferenceISBN
0-262-58217-1Department affiliated with
- Informatics Publications
Full text available
- No
Peer reviewed?
- Yes
Legacy Posted Date
2012-02-06Usage metrics
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