Abductive Logic Programming with CIFF
Ulle Endriss, Paolo Mancarella, Fariba Sadri, Giacomo Terreni, Francesca Toni · 2004
Introduction Abduction has found broad application as a powerful tool for hypothetical reasoning with incomplete knowledge, which can be handled by labelling some pieces of information as abducibles, i.e. as possible hypotheses which can be assumed to hold, provided that they are consistent with the given knowledge base. Abductive Logic Programming (ALP) [4] combines abduction with logic programming enriched with integrity constraints to further restrict the range of possible hypotheses. We introduce a new proof procedure for abductive logic programming which we call CIFF. Our procedure extends the IFF procedure of Fung and Kowalski [3] by integrating abductive reasoning with constraint solving. Another feature of our approach is that we do not attempt to provide a static characterisation of the class of allowed inputs on which the procedure can operate correctly, but rather check allowedness dynamically during a derivation. This allows us to cover a larger class of inputs. 2 Abduct