Markov logic networks (MLNs) combine logic and probability by attaching weights to first-order clauses, and viewing these as templates for features of Markov networks. In this pap...
Abstract. Statistical relational models, such as Markov logic networks, seek to compactly describe properties of relational domains by representing general principles about objects...
Markov Logic Networks (MLNs) have emerged as a powerful framework that combines statistical and logical reasoning; they have been applied to many data intensive problems including...
Goal recognition in digital games involves inferring players’ goals from observed sequences of low-level player actions. Goal recognition models support player-adaptive digital ...
Eun Y. Ha, Jonathan P. Rowe, Bradford W. Mott, Jam...
Transfer learning addresses the problem of how to leverage knowledge acquired in a source domain to improve the accuracy and speed of learning in a related target domain. This pap...
Lilyana Mihalkova, Tuyen N. Huynh, Raymond J. Moon...