Many real-world applications of AI require both probability and first-order logic to deal with uncertainty and structural complexity. Logical AI has focused mainly on handling com...
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. A new method is proposed for compiling causal independencies into Markov logic networks (MLNs). An MLN can be viewed as compactly representing a factorization of a joint ...
Sriraam Natarajan, Tushar Khot, Daniel Lowd, Prasa...
A recurrent question in the design of intelligent agents is how to assign degrees of beliefs, or subjective probabilities, to various events in a relational environment. In the sta...
Class syntax can be used to 1) model temporal or locational evolvement of class labels of feature observation sequences, 2) correct classification errors of static classifiers if ...