A Constraint Logic Programming Approach for Computing Ordinal
  Conditional Functions
  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.
