ACM SIGART Bulletin
SHAPES: A Novel Approach for Learning Search Heuristics in Under-Constrained Optimization Problems
IEEE Transactions on Knowledge and Data Engineering
Probabilistic Explanation Based Learning
ECML '07 Proceedings of the 18th European conference on Machine Learning
The complexity of theory revision
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
The complexity of theory revision
Artificial Intelligence
Learning without human expertise: a case study of the double dummy bridge problem
IEEE Transactions on Neural Networks
Integrating induction and abduction in logic programming
Information Sciences: an International Journal
A theory of unsupervised speedup learning
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
Finding accurate frontiers: a knowledge-intensive approach to relational learning
AAAI'93 Proceedings of the eleventh national conference on Artificial intelligence
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One problem which frequently surfaces when applying explanation-based learning (EBL) to imperfect theories is the multiple inconsistent explanation problem. The multiple inconsistent explanation problem occurs when a domain theory produces multiple explanations for a training instance, only some of which are correct. Domain theories which suffer from the multiple inconsistent explanation problem can occur in many different contexts, such as when some information is missing and must be assumed: since such assumptions can be incorrect, incorrect explanations can be constructed. This paper proposes an extension of explanation-based learning, called abductive explanation-based learning (A-EBL) which solves the multiple inconsistent explanation problem by using set covering techniques and negative examples to choose among the possible explanations of a training example. It is shown by formal analysis that A-EBL has convergence properties that are only logarithmically worse than EBL/TS, a formalization of a certain type of knowledge-level EBL; A-EBL is also proven to be computationally efficient, assuming that the domain theory is tractable. Finally, experimental results are reported on an application of A-EBL to learning correct rules for opening bids in the game of contract bridge given examples and an imperfect domain theory.