We propose a logical/mathematical framework for statistical parameter learning of parameterized logic programs, i.e. denite clause programs containing probabilistic facts with a ...
A basic requirement of declarative programming is to free the programmer from the need to manually insert control annotations into the program. Here we study the execution of logi...
Open Answer Set Programming (OASP) is an attractive framework for integrating ontologies and rules. Although several decidable fragments of OASP have been identified, few reasonin...
Two approaches to logic programming with probabilities emerged over time: bayesian reasoning and probabilistic satisfiability (PSAT). The attractiveness of the former is in tying ...
We present the system LAP (Learning Abductive Programs) that is able to learn abductive logic programs from examples and from a background abductive theory. A new type of induction...