Implementing Probabilistic Abductive Logic Programming with Constraint Handling Rules [chapter]

Henning Christiansen
2008 Lecture Notes in Computer Science  
A class of Probabilistic Abductive Logic Programs (PALPs) is introduced and an implementation is developed in CHR for solving abductive problems, providing minimal explanations with their probabilities. Both all-explanations and most-probable-explanations versions are given. Compared with other probabilistic versions of abductive logic programming, the approach is characterized by higher generality and a flexible and adaptable architecture which incorporates integrity constraints and
more » ... with external constraint solvers. A PALP is transformed in a systematic way into a CHR program which serves as a query interpreter, and the resulting CHR code describes in a highly concise way, the strategies applied in the search for explanations. As usual, an arbitrary and infinite collection of function symbols, including constants, are assumed and atoms defined in the standard way. Notice that ⊥ is a distinguished predicate rather that a representation of falsity. The relationship |= refers to the usual, completion-based semantics for logic programs [37, 31] ; for external predicates, we assume a semantics independently of
doi:10.1007/978-3-540-92243-8_5 fatcat:qx5pefg2gzaxtkzejesfrcpczq