CRISNER: A Practically Efficient Reasoner for Qualitative Preferences
This tool addresses the need for practical and reliable preference reasoning in AI applications, though it is incremental as it builds on existing formalisms.
The authors tackled the problem of efficiently reasoning about qualitative preferences in ceteris paribus languages by developing CRISNER, a tool that provides exact and provably correct query answering for tasks like dominance testing and consistency checking.
We present CRISNER (Conditional & Relative Importance Statement Network PrEference Reasoner), a tool that provides practically efficient as well as exact reasoning about qualitative preferences in popular ceteris paribus preference languages such as CP-nets, TCP-nets, CP-theories, etc. The tool uses a model checking engine to translate preference specifications and queries into appropriate Kripke models and verifiable properties over them respectively. The distinguishing features of the tool are: (1) exact and provably correct query answering for testing dominance, consistency with respect to a preference specification, and testing equivalence and subsumption of two sets of preferences; (2) automatic generation of proofs evidencing the correctness of answer produced by CRISNER to any of the above queries; (3) XML inputs and outputs that make it portable and pluggable into other applications. We also describe the extensible architecture of CRISNER, which can be extended to new reference formalisms based on ceteris paribus semantics that may be developed in the future.