Efficient multiple predicate learner based on fast failure mechanism
We present a multiple predicate learner (MPL-Core) which efficiently induces some Horn clauses from example sets of multiple predicates and relative background knowledge. Core, a single predicate learning module, has a fast failure mechanism, and can select refinement operators based on the learning task. By means of GPC, an efficient pruning method, Core effectively prunes unpromising branches in a search tree, making the search space a rational volume. MPL-Core employs both the intensional and extensional learning style in the induction of target predicates. Furthermore, our system with the fast failure mechanism gives a distinct improvement over the existing multiple predicate learning systems in the computational complexity.
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- 1.R. A. Caruana. Multitask learning: A knowledge-based source of inductive bias. In Proc. of 10th International Conference on Machine Learning, pages 41–48. Morgan Kaufmann, 1993.Google Scholar
- 2.L. De Raedt, N. Lavrač, and S. Džeroski. Multiple predicate learning. In Proc. Thirteenth International Joint Conference on Artificial Intelligence, San Mateo, CA, 1993. Morgan Kaufmann.Google Scholar
- 3.D. F. Gordon and M. Desjardins. Evaluation and selection of biases in machine learning. Machine Learning, 20:5–22, 1995.Google Scholar
- 4.B. Kijsirikul, M. Numao, and M. Shimura. Efficient learning of logic programs with non-determinate non-discriminating literals. In Proc. Eighth International Workshop on Machine Learning, pages 417–421, San Mateo, CA, 1991. Morgan Kaufmann.Google Scholar
- 5.B. Kijsirikul, M. Numao, and M. Shimura. Discrimination-based constructive induction of logic programs. In Proc. Tenth National Conference on Artificial Intelligence, San Mateo, CA, 1992. Morgan Kaufmann.Google Scholar
- 6.S.H. Muggleton and C. Feng. Efficient induction of logic programs. In Proc. First Conference on Algorithmic Learning Theory, pages 368–381, Tokyo, 1990. Ohmsha.Google Scholar
- 7.J.R. Quinlan. Learning logical definitions from relations. Machine Learning, 5(3):239–266, 1990.Google Scholar
- 8.B. L. Richard and R. J. Mooney. Automatic refinement of first-order horn-clause domain theories. Machine Learning, 19:95–131, 1995.Google Scholar
- 9.G. Silverstein and M. Pazzani. Relational cliches: Constraining constructive induction during. In Proc. of Eighth International Conference on Machine Learning, pages 203–207. Morgan Kaufmann, 1991.Google Scholar
- 10.S. Tangkitvanich and M. Shimura. Refining a relational theory with multiple faults in the concept and subconcepts. In Proc. Ninth International Conference on Machine Learning, pages 436–444, San Mateo, CA, 1992. Morgan Kaufmann.Google Scholar