Coarse-to-fine approaches use sequences of increasingly fine approximations to control the complexity of inference and learning. These techniques are often used in NLP and visio...
Tractable subsets of first-order logic are a central topic in AI research. Several of these formalisms have been used as the basis for first-order probabilistic languages. Howev...
We introduce First Order ProbLog, an extension of first order logic with soft constraints where formulas are guarded by probabilistic facts. The paper defines a semantics for FOPro...
Maurice Bruynooghe, Theofrastos Mantadelis, Angeli...
Unifying first-order logic and probability is a long-standing goal of AI, and in recent years many representations combining aspects of the two have been proposed. However, infere...
First-order probabilistic logic is a powerful knowledge representation language. Unfortunately, deductive reasoning based on the standard semantics for this logic does not support...