Abstract
A continuing problem with Inductive Logic Programming (ILP) [1] has been the poor handling of numbers. Constraint Inductive Logic Programming (CILP) aims to solve this problem. We propose a new approach to CILP, and implement a prototype of CILP system called BPU-CILP. In our approach, methods from pattern recognition, such as Fisher’s linear discriminant [2] and prototype-based partitional clustering [3], are introduced into CILP. BPU-CILP can generate various forms of polynomial constraints in multiple dimensions, without additional background knowledge. As results, a CLP program covering all positive examples and consistent with all negative examples can be automatically derived.
The work is supported by the Natural Science Foundation of China (60173014) and Beijing Municipal Natural Science Foundation (4022003).
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
Nienhuys-Cheng, S.-H., de Wolf, R.: Foundations of Inductive Logic Programming. LNCS, vol. 1228, pp. 163–177. Springer, Heidelberg (1997)
Devijver, P.A., Kittler, J.: Pattern Recognition: A Statical Approach. Prentice-Hall, New York (1982)
Dumitrescu, D., Lazzerini, B., Jain, L.C.: Fuzzy sets and their application to clustering and training. CRC Press, Boca Raton (2000)
Muggleton, S., Page, C.D.: Beyond first-order learning: inductive logic programming with higher-order logic. Technical Report PRG-TR-13-94, Oxford University, Oxford (1994)
Srinivasan, A., Camacho, R.: Experiments in numeric reasoning with inductive logic programming. Technical Report PRG-TR-22-96, Oxford University, Oxford (1996)
Sebag, M., Rouveirol, C.: Constraint Inductive Logic Programming. In: de Raedt (ed.) Advances in ILP, pp. 277–294. IOS Press, Amsterdam (1996)
Anthony, S., Frisch, A.: Generating numerical literals during refinement. In: Džeroski, S., Lavrač, N. (eds.) ILP 1997. LNCS, vol. 1297, pp. 61–76. Springer, Heidelberg (1997)
Quinlan, J.R.: Learning logical definitions from relations. Machine Learning 5, 239–266 (1990)
http://axon.cs.byu.edu/~martinez/classes/470/MLDB/balance-scale/
Everitt, B.S., Dunn, G.: Applied Multivariate Data Analysis. Arnold, London (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zheng, L., Liu, C., Jia, D., Zhong, N. (2003). A New Approach to Constraint Inductive Logic Programming. In: Zhong, N., Raś, Z.W., Tsumoto, S., Suzuki, E. (eds) Foundations of Intelligent Systems. ISMIS 2003. Lecture Notes in Computer Science(), vol 2871. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39592-8_50
Download citation
DOI: https://doi.org/10.1007/978-3-540-39592-8_50
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-20256-1
Online ISBN: 978-3-540-39592-8
eBook Packages: Springer Book Archive