Abstract
Procedural and Declarative knowledge play a key role in cognitive architectures for robots. These types of architectures use the human brain as inspiration to design control structures that allow robots to be fully autonomous, in the sense that their development depends only on their own experience in the environment. The two main components that make up cognitive architectures are models (prediction) and action-selection structures (decision). Models represent the declarative knowledge the robot acquires during its lifetime. On the other hand, action-selection structures represent the procedural knowledge, and its autonomous acquisition depends on the quality of the models that are being learned concurrently. The coupled learning of models and action-selection structures is a key aspect in robot development, and it has been rarely studied in the field. This work aims to start filling this gap by analyzing how these concurrent learning processes affect each other using an evolutionary-based cognitive architecture, the Multilevel Darwinist Brain, in a simulated robotic experiment
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Anderson, J.R., Bothell, D., Byrne, M.D., Douglass, S., Lebiere, C., Qin, Y.: An Integrated Theory of the Mind. Psychological Review 111(4), 1036–1060 (2004)
Asada, M., MacDorman, K.F., Ishiguro, H., Kuniyoshi, Y.: Cognitive developmental robotics as a new paradigm for the design of humanoid robots. Robotics and Autonomous Systems 37, 185–193 (2001)
Bach, S.: Principles of Synthetic Intelligence. PSI: An Architecture of Motivated Cognition. Oxford Univ. Press (2009)
Bellas, F., Duro, R.J., Faiña, A., Souto, D.: Multilevel Darwinist Brain (MDB): Artificial Evolution in a Cognitive Architecture for Real Robots. IEEE Transactions on Autonomous Mental Development 2(4), 340–354 (2010)
Bellas, F., Caamaño, P., Faiña, A., Duro, R.J.: Dynamic learning in cognitive robotics through a procedural long term memory. Evolving Systems 5(1), 49–63 (2014)
Cotterill, R.: Enchanted looms: Conscious networks in brains and computers. Cambridge University Press (2000)
Duro, R.J., Bellas, F., Becerra, J.A.: Brain-Like Robotics. Springer Handbook of Bio-/Neuroinformatics, pp. 1019–1056 (2014)
Floreano, D., Dürr, P., Mattiussi, C.: Neuroevolution: from architectures to learning. Evolutionary Intelligence 1(2008), 47–62 (2008)
Goertzel, B., de Garis, H.: XIA-MAN: An extensible, integrative architecture for intelligent humanoid robotics. AAAI Fall Symp. Biol. Inspired Cogn. Archit., 65–74 (2008)
Hesslow, G.: The current status of the simulation theory of cognition. Brain Research 1428, 71–79 (2012)
Krichmar, J.L., Edelman, G.M.: Principles underlying the construction of brain-based devices. In: Proceedings of AISB 2006, vol. 2, pp. 37–42 (2006)
Laird, J.: The Soar Cognitive Architecture. MIT Press (2012)
Weng, J., McClelland, J., Pentland, A., Sporns, O., Stockman, I., Sur, M., Thelen, E.: Autonomous mental development by robots and animals. Science 291, 599–600 (2001)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Salgado, R., Bellas, F., Duro, R.J. (2015). Studying the Coupled Learning of Procedural and Declarative Knowledge in Cognitive Robotics. In: Wilson, S., Verschure, P., Mura, A., Prescott, T. (eds) Biomimetic and Biohybrid Systems. Living Machines 2015. Lecture Notes in Computer Science(), vol 9222. Springer, Cham. https://doi.org/10.1007/978-3-319-22979-9_30
Download citation
DOI: https://doi.org/10.1007/978-3-319-22979-9_30
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-22978-2
Online ISBN: 978-3-319-22979-9
eBook Packages: Computer ScienceComputer Science (R0)