A Constraint Logic Programming Approach for Computing Ordinal Conditional Functions

Abstract

In order to give appropriate semantics to qualitative conditionals of the form "if A then normally B", ordinal conditional functions (OCFs) ranking the possible worlds according to their degree of plausibility can be used. An OCF accepting all conditionals of a knowledge base R can be characterized as the solution of a constraint satisfaction problem. We present a high-level, declarative approach using constraint logic programming techniques for solving this constraint satisfaction problem. In particular, the approach developed here supports the generation of all minimal solutions; these minimal solutions are of special interest as they provide a basis for model-based inference from R.