Markov logic networks (MLNs) combine first-order logic and Markov networks, allowing us to handle the complexity and uncertainty of real-world problems in a single consistent fram...
Multi-label learning deals with ambiguous examples each may belong to several concept classes simultaneously. In this learning framework, the inherent ambiguity of each example is...
Many real-world problems are characterized by complex relational structure, which can be succinctly represented in firstorder logic. However, many relational inference algorithms ...
Most algorithms for computing diagnoses within a modelbased diagnosis framework are deterministic. Such algorithms guarantee soundness and completeness, but are P 2 hard. To overc...
Alexander Feldman, Gregory M. Provan, Arjan J. C. ...
We present a new probabilistic framework for finding likely variable assignments in difficult constraint satisfaction problems. Finding such assignments is key to efficient sea...
Eric I. Hsu, Matthew Kitching, Fahiem Bacchus, She...