Abstract
Finding sequential concepts, as in planning, is a complex task because of the exponential size of the search space. Empirical learning can be an effective way to find sequential concepts from observations. Sequential Instance-Based Learning (SIBL), which is presented here, is an empirical learning paradigm, modeled after Instance-Based Learning (IBL) that learns sequential concepts, ordered sequences of state-action pairs to perform a synthesis task. SIBL is highly effective and learns expert-level knowledge. SIBL demonstrates the feasibility of using an empirical learning approach to discover sequential concepts. In addition, this approach suggests a general framework that systematically extends empirical learning to learning sequential concepts. SIBL is tested on the domain of bridge.
The author’s new postal and email addresses are: 4476 Hock Maple Ct. Concord, CA 94521, USA. jenngang@ix.netcom.com
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
Agrawal, R., Srikant, R. (1995). “Mining Sequential Patterns.” In Proceedings of the Eleventh International Conference on Data Engineering (ICDE’95), Taipei, Taiwan, 3–14.
Aha,D. W. (1992). “Tolerating noisy, irrelevant and novel attributes in instance-based learning algorithm.” International Journal of Man-Machine Studies, 36, 267–287.
Breiman,L., Friedman, J., Olshen, R., Stone, C. (1984). Classification and Regression Trees, Wadsworth International Group.
Clancey,W. J. (1985). “Heuristic Classification.” Artificial Intelligence, 27, 289–350.
Epstein,S. L. and Shih, J. (1998). “.” In Proceedings 12th Biennial Conference of the Canadian Society for Computational Studies of intelligence (AI’98), Vancouver, BC, Canada, 442–454.
Fayyad,U. M., Piatetsky-Shapiro, G., Smyth, P., and Uthurusamy, R., eds. (1996). Advances in Knowledge Discovery and Data Mining: AAAI Press.
Ginsberg, M. (dy1999). “GIB: Steps towards an expert-level bridge-playing program.” In Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence (IJCAI-99), Stockholm, Sweden, 584–590.
Kaelbling,L. P., Littman, M. L., and Moore, A. W. (1996). “Reinforcement Learning: A Survey.” Journal of Artificial Intelligence Research, 4, 237–285.
Kibler, D., and Langley, P. (1988). “Machine Learning as an Experimental Science.” Machine Learning, 3(1), 5–8.
McCallum,R. A. (1995). “Instance-Based Utile Distinctions for Reinforcement Learning with Hidden State.” In Proceedings of the Twelfth International Conference on Machine Learning, Tahoe City, CA.
McCarthy, J. and Patrick J. Hayes, 1969. Some philosophical problems from the standpoint of artificial intelligence. In Machine Intelligence 4, ed. B. Meltzer and D. Michie. Edinburgh: Edinburgh University Press.
Qian,N., Sejnowski, T.J. (1988). “Predicting the secondary structure of globular proteins using neural network models.” Journal of Molecular Biology, 202, 865–884.
Quinlan,J. R. (1986). “Induction of Decision Trees.” Machine Learning, 1, 81–106.
Rabiner,L. R. (1989). “A tutorial on Hidden Markov Models and selected applications in speech recognition.” Proceedings of the IEEE, 77(2), 257–285.
Salzberg,S. L. (1991). “A nearest hyperrectangle learning method.” Machine Learning, 6, 251–276.
Winston,P. H. (1975). “Learning structural descriptions from examples.” The Psychology of Computer Vision, P. H. Winston, ed., McGraw-Hill, New York.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2001 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Shih, J. (2001). sequential Instance-Based Learning for Planning in the Context of an Imperfect Information Game. In: Aha, D.W., Watson, I. (eds) Case-Based Reasoning Research and Development. ICCBR 2001. Lecture Notes in Computer Science(), vol 2080. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44593-5_34
Download citation
DOI: https://doi.org/10.1007/3-540-44593-5_34
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-42358-4
Online ISBN: 978-3-540-44593-7
eBook Packages: Springer Book Archive