Probabilistic Explanation Based Learning

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.

[1]  Luc De Raedt,et al.  Revising Probabilistic Prolog Programs , 2006, ILP.

[2]  Luc De Raedt,et al.  Probabilistic logic learning , 2003, SKDD.

[3]  P. Bork,et al.  Association of genes to genetically inherited diseases using data mining , 2002, Nature Genetics.

[4]  Neil D. Lawrence,et al.  Missing Data in Kernel PCA , 2006, ECML.

[5]  Luc De Raedt,et al.  Compressing probabilistic Prolog programs , 2007, Machine Learning.

[6]  Peter Norvig,et al.  Artificial intelligence - a modern approach, 2nd Edition , 2003, Prentice Hall series in artificial intelligence.

[7]  Tom M. Mitchell,et al.  Explanation-Based Generalization: A Unifying View , 1986, Machine Learning.

[8]  A. Antunes.  Democracia e Cidadania na Escola: Do Discurso à Prática , 2008 .

[9]  William W. Cohen.  Abductive explanation-based learning: A solution to the multiple inconsistent explanation problem , 1992, Machine Learning.

[10]  Ben Taskar,et al.  Introduction to statistical relational learning , 2007 .

[11]  Gerald DeJong,et al.  Toward Robust Real-World Inference: A New Perspective on Explanation-Based Learning , 2006, ECML.

[12]  Pat Langley,et al.  Unifying Themes in Empirical and Explanation-Based Learning , 1989, ML.

[13]  Haym Hirsh,et al.  Explanation-based Generalization in a Logic-Programming Environment , 1987, IJCAI.

[14]  David Poole,et al.  Probabilistic Horn Abduction and Bayesian Networks , 1993, Artif. Intell..

[15]  Luc De Raedt,et al.  ProbLog: A Probabilistic Prolog and its Application in Link Discovery , 2007, IJCAI.

[16]  Hannu Toivonen,et al.  Link Discovery in Graphs Derived from Biological Databases , 2006, DILS.

[17]  Alan Bundy,et al.  Explanation-Based Generalisation = Partial Evaluation , 1988, Artif. Intell..

[18]  Peter Norvig,et al.  Artificial Intelligence: A Modern Approach , 1995 .

[19]  Yoshitaka Kameya,et al.  Parameter Learning of Logic Programs for Symbolic-Statistical Modeling , 2001, J. Artif. Intell. Res..