Abstract
One of the most essential properties of a cognitive being is its ability to learn. Learningis the “process of acquiring modifications in existing knowledge, skills, habits,or tendencies through experience, practice, or exercise” (Encyclopædia Britannica, 2007).
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
Kaelbling, L.P., Littmann, M.L., Moore, A.W.: Reinforcement learning: A survey. Journal of Artificial Intelligence Research 4, 237–285 (1996)
Sloman, A.: Why we need many knowledge representation formalisms. In: Bramer, M.A. (ed.) Research and Development in Expert Systems – Proceedings of the BCS Expert Systems Conference, pp. 163–183. Springer (1985)
Sutton, R.S., Barto, A.G.: Reinforcement learning: an introduction. Adaptive Computation and Machine Learning. MIT Press, Cambridge, MA (1998)
Thrun, S., Schwartz, A.: Finding structure in reinforcement learning. In: Tesauro, G., Touretzky,D., Leen, T. (eds.) Advances in Neural Information Processing Systems: Proceedings of the 1994 Conference, vol. 7. MIT Press, Cambridge, MA (1995)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Frommberger, L. (2010). Introduction. In: Qualitative Spatial Abstraction in Reinforcement Learning. Cognitive Technologies. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16590-0_1
Download citation
DOI: https://doi.org/10.1007/978-3-642-16590-0_1
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-16589-4
Online ISBN: 978-3-642-16590-0
eBook Packages: Computer ScienceComputer Science (R0)