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Abstract

Research over the past several decades in learning logical and probabilistic models has greatly increased the range of phenomena that machine learning can address. Recent work has extended these boundaries even further by unifying these two powerful learning frameworks. However, new frontiers await. Current techniques are capable of learning only a subset of the knowledge needed by practitioners in important domains, and further unification of probabilistic and logical learning offers a unique ability to produce the full range of knowledge needed in a wide range of applications.

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