Abstract
In this work, a hybrid, self-configurable, multilayered and evolutionary architecture for cognitive agents is developed. Each layer of the subsumption architecture is modeled by one different Machine Learning System MLS based on bio-inspired techniques. In this research an evolutionary mechanism supported on Gene Expression Programming to self-configure the behaviour arbitration between layers is suggested. In addition, a co-evolutionary mechanism to evolve behaviours in an independent and parallel fashion is used. The proposed approach was tested in an animat environment using a multi-agent platform and it exhibited several learning capabilities and emergent properties for self-configuring internal agent’s architecture.
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References
R.A. Brooks, A Robust Layered Control System For A Mobile Robot, IEEE Journal Of Robotics And Automation, RA-2 (1986), 14–23.
S.W. Wilson, State of XCS Classifier System Research, Lecture Notes in Computer Science, 1813 (2000), 63–81.
L. N. de Castro, J. Timmis, Artificial Immune Systems: A New Computational Intelligence Approach, Ed. Springer (2002)
V. Kuzmin, Connectionist Q-learning in Robot Control Task, Proceedings of Riga Technical University (2002), 112–121.
J.H. Holland, Induction, Processes of Inference, Learning and Discovery, Mich: Addison-Wesley (1953).
Farahmand, Hybrid Behavior Co-evolution and Structure Learning in Behavior-based Systems, IEEE Congress on EC, Vancouver (2006), 979–986.
Ferreira, Gene Expression Programming: A new adaptive algorithm for solving problems, Complex Systems, forthcoming, (2001).
P. Stone, Layered Learning in Multiagent Systems, Thesis CS-98-187 (1998)
D. Romero, L. Niño, An Immune-based Multilayered Cognitive Model for Autonomous Navigation, IEEE Congress on EC, Vancouver (2006), 1115–1122.
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© 2007 Springer-Verlag Berlin Heidelberg
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Romero, O.J., de Antonio, A. (2007). Analysis of Emergent Properties in a Hybrid Bio-inspired Architecture for Cognitive Agents. In: Corchado, E., Corchado, J.M., Abraham, A. (eds) Innovations in Hybrid Intelligent Systems. Advances in Soft Computing, vol 44. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74972-1_2
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DOI: https://doi.org/10.1007/978-3-540-74972-1_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74971-4
Online ISBN: 978-3-540-74972-1
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