A Constraint Logic Programming Approach for Computing Ordinal Conditional Functions

TL;DR

This paper introduces a declarative constraint logic programming method for computing ordinal conditional functions, enabling the generation of minimal solutions for qualitative conditionals to support model-based inference.

Contribution

It presents a novel high-level approach using CLP techniques to solve the constraint satisfaction problem for OCFs, including generating all minimal solutions.

Findings

Supports generation of all minimal solutions

Facilitates model-based inference from knowledge bases

Uses declarative CLP approach for OCF computation

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.