Logical Conditional Preference Theories

TL;DR

This paper introduces logical conditional preference theories (LCP theories), a new framework that generalizes CP-nets by using Datalog programs to express preferences, enabling richer semantic and algorithmic capabilities.

Contribution

It proposes a novel preference modeling framework that unifies and extends existing approaches using Datalog, enhancing expressiveness and computational tools.

Findings

LCP theories can represent complex preferences beyond CP-nets.

They leverage Datalog semantics for improved reasoning.

The framework unifies various conditional preference models.

Abstract

CP-nets represent the dominant existing framework for expressing qualitative conditional preferences between alternatives, and are used in a variety of areas including constraint solving. Over the last fifteen years, a significant literature has developed exploring semantics, algorithms, implementation and use of CP-nets. This paper introduces a comprehensive new framework for conditional preferences: logical conditional preference theories (LCP theories). To express preferences, the user specifies arbitrary (constraint) Datalog programs over a binary ordering relation on outcomes. We show how LCP theories unify and generalize existing conditional preference proposals, and leverage the rich semantic, algorithmic and implementation frameworks of Datalog.