Probabilistic Explanation Based Learning

摘要

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 similar examples 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.