Hybrid behaviour orchestration in a multilayered cognitive architecture using an evolutionary approach

Managing and arbitrating behaviours, processes and components in multilayered cognitive architectures when a huge amount of environmental variables are changing continuously with increasing complexity, ensue in a very comprehensive task. The presented framework proposes an hybrid cognitive architect...

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Veröffentlicht in:2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence) S. 174 - 180
Hauptverfasser: Lopez, O.J.R., de Antonio, A.
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.06.2008
Schlagworte:
Evolutionary computation
ISBN:1424418224, 9781424418220
ISSN:1089-778X
Online-Zugang:Volltext
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Zusammenfassung:Managing and arbitrating behaviours, processes and components in multilayered cognitive architectures when a huge amount of environmental variables are changing continuously with increasing complexity, ensue in a very comprehensive task. The presented framework proposes an hybrid cognitive architecture that relies on subsumption theory and includes some important extensions. These extensions can be condensed in inclusion of learning capabilities through bio-inspired reinforcement machine learning systems, an evolutionary mechanism based on gene expression programming to self-configure the behaviour arbitration between layers, a co-evolutionary mechanism to evolve behaviour repertories in a parallel fashion and finally, an aggregation mechanism to combine the learning algorithms outputs to improve the learning quality and increase the robustness and fault tolerance ability of the cognitive agent. The proposed architecture was proved in an animat environment using a multi-agent platform where several learning capabilities and emergent properties for self-configuring internal agentpsilas architecture arise.
ISBN:1424418224
9781424418220
ISSN:1089-778X
DOI:10.1109/CEC.2008.4630795