Explanation-based generalisation = partial evaluation
Artificial Intelligence
Unifying themes in empirical and explanation-based learning
Proceedings of the sixth international workshop on Machine learning
Probabilistic Horn abduction and Bayesian networks
Artificial Intelligence
Explanation-Based Generalization: A Unifying View
Machine Learning
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
ACM SIGKDD Explorations Newsletter
Compressing probabilistic Prolog programs
Machine Learning
Parameter learning of logic programs for symbolic-statistical modeling
Journal of Artificial Intelligence Research
Explanation-based generalization in a logic-programming environment
IJCAI'87 Proceedings of the 10th international joint conference on Artificial intelligence - Volume 1
ProbLog: a probabilistic prolog and its application in link discovery
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Toward robust real-world inference: a new perspective on explanation-based learning
ECML'06 Proceedings of the 17th European conference on Machine Learning
Link discovery in graphs derived from biological databases
DILS'06 Proceedings of the Third international conference on Data Integration in the Life Sciences
Parameter Learning in Probabilistic Databases: A Least Squares Approach
ECML PKDD '08 Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I
On the Efficient Execution of ProbLog Programs
ICLP '08 Proceedings of the 24th International Conference on Logic Programming
On the implementation of the probabilistic logic programming language problog
Theory and Practice of Logic Programming
Abductive plan recognition by extending Bayesian logic programs
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part II
Patterns and logic for reasoning with networks
Bisociative Knowledge Discovery
Improving robustness and flexibility of concept taxonomy learning from text
NFMCP'12 Proceedings of the First international conference on New Frontiers in Mining Complex Patterns
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Explanation based learning produces generalized explanations from examples. These explanations are typically built in a deductive manner and they aim to capture the essential characteristics of the examples.Probabilistic explanation based learning extends this idea to probabilistic logic representations, which have recently become popular within the field of statistical relational learning. The task is now to find the most likely explanation why one (or more) example(s) satisfy a given concept. These probabilistic and generalized explanations can then be used to discover similarexamples and to reason by analogy. So, whereas traditional explanation based learning is typically used for speed-up learning, probabilistic explanation based learning is used for discovering new knowledge.Probabilistic explanation based learning has been implemented in a recently proposed probabilistic logic called ProbLog, and it has been applied to a challenging application in discovering relationships of interest in large biological networks.