We propose a logical/mathematical framework for statistical parameter learning of parameterized logic programs, i.e. denite clause programs containing probabilistic facts with a ...
Abstract. Statistical relational models, such as Markov logic networks, seek to compactly describe properties of relational domains by representing general principles about objects...
Languages that combine predicate logic with probabilities are needed to succinctly represent knowledge in many real-world domains. We consider a formalism based on universally qua...
In the paper we establish the xed-parameter complexity for several parameterized decision problems involving models, supported models and stable models of logic programs. We also e...
PRISM is a probabilistic extension of Prolog. It is a high level language for probabilistic modeling capable of learning statistical parameters from observed data. After reviewing ...