Logical foundations of artificial intelligence
Logical foundations of artificial intelligence
Multistrategy Learning and Theory Revision
Machine Learning - Special issue on multistrategy learning
Theory refinement combining analytical and empirical methods
Artificial Intelligence
Machine Learning
Knowledge-based artificial neural networks
Artificial Intelligence
Parameter Estimation in Stochastic Logic Programs
Machine Learning
Inductive Logic Programming: Techniques and Applications
Inductive Logic Programming: Techniques and Applications
Explanation-Based Generalization: A Unifying View
Machine Learning
Explanation-Based Learning: An Alternative View
Machine Learning
Revising Engineering Models: Combining Computational Discovery with Knowledge
ECML '02 Proceedings of the 13th European Conference on Machine Learning
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
ACM SIGKDD Explorations Newsletter
nFOIL: integrating Naïve Bayes and FOIL
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
On the hardness of approximate reasoning
IJCAI'93 Proceedings of the 13th international joint conference on Artifical intelligence - Volume 1
The complexity of theory revision
Artificial Intelligence
A declarative approach to bias in concept learning
AAAI'87 Proceedings of the sixth National conference on Artificial intelligence - Volume 2
Analytic models and empirical search: a hybrid approach to code optimization
LCPC'05 Proceedings of the 18th international conference on Languages and Compilers for Parallel Computing
Probabilistic Explanation Based Learning
ECML '07 Proceedings of the 18th European conference on Machine Learning
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Over the last twenty years AI has undergone a sea change. The once-dominant paradigm of logical inference over symbolic knowledge representations has largely been supplanted by statistical methods. The statistical paradigm affords a robustness in the real-world that has eluded symbolic logic. But statistics sacrifices much in expressiveness and inferential richness, which is achieved by first-order logic through the nonlinear interaction and combinatorial interplay among quantified component sentences. We present a new form of Explanation Based Learning in which inference results from two forms of evidence: analytic (support via sound derivation from first-order representations of an expert's conceptualization of a domain) and empirical (corroboration derived from consistency with real-world observations). A simple algorithm provides a first illustration of the approach. Some important properties are proven including tractability and robustness with respect to the real world.