Logical Conditional Preference Theories
This provides a more flexible and unified framework for conditional preferences, impacting areas like constraint solving, but it is incremental as it builds on existing CP-net literature.
The paper tackles the problem of expressing qualitative conditional preferences by introducing Logical Conditional Preference Theories (LCP theories), which unify and generalize existing frameworks like CP-nets using Datalog programs, resulting in a comprehensive new approach.
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.