Implementing probabilistic abductive logic programming with Constraint Handling Rules

Abstract. 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

The CHR-based implementation of a system for generation and confirmation of hypotheses

Hypothetical reasoning makes it possible to reason with incomplete information in a wide range of knowledge-based applications. It is usually necessary to constrain the generation of hypotheses, so to avoid inconsistent sets or to infer new hypotheses from already made ones. These requirements are met by several abductive frameworks. In order to tackle many practical cases, however, it would also be desirable to support the dynamical acquisition of new facts, which can confirm the hypotheses, or possibly disconfirm them, leading to the generation of alternative sets of hypotheses. In this paper, we present a system which supports the generation of hypotheses, as well as their confirmation or disconfirmation. We also describe the implementation of an abductive proof procedure, used as a reasoning engine for the generation and (dis)confirmation of hypotheses