Implementing Probabilistic Abductive Logic Programming with Constraint Handling Rules

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 interaction
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.