Abstract
This paper describes our efforts in the DARPA High Performance Knowledge Bases (HPKB) program to solve a difficult military planning problem. We describe a case-based reasoning solution within a knowledge-rich environment that leverages both examples and rule-based knowledge. Our solution uses an innovative combination of nearest neighbor, neural networks, and natural deduction. Results from a DARPA-sponsored evaluation and our own experiments demonstrate the utility of our solution and the contribution of the technology components.
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References
Emde, W. (1996). Relational instance-based learning. In Proceedings of the 13th International Conference on Machine Learning.
Gebhardt, F. (1997). Survey on structure-based case retrieval. The Knowledge Engineering Review, 12(1), 41–58.
Jones, E. (1999). HPKB course of action challenge problem specification. Tech. rep., Alphatech Inc.
Kolodner, J. L. (1993). Case-Based Reasoning. Morgan Kaufmann.
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© 2000 Springer-Verlag Berlin Heidelberg
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Moriarty, D.E. (2000). Determining Effective Military Decisive Points through Knowledge-Rich Case-Based Reasoning. In: Logananthara, R., Palm, G., Ali, M. (eds) Intelligent Problem Solving. Methodologies and Approaches. IEA/AIE 2000. Lecture Notes in Computer Science(), vol 1821. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45049-1_55
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DOI: https://doi.org/10.1007/3-540-45049-1_55
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Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-67689-8
Online ISBN: 978-3-540-45049-8
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