Nonmonotonic Reasoning via Possibility Theory
This addresses foundational issues in AI reasoning for knowledge representation, but appears incremental as it builds on existing possibility theory frameworks.
The paper tackles the problem of representing default knowledge in nonmonotonic reasoning by introducing the operation of possibility qualification, applying it to prototypical problems like the Yale shooting problem to investigate its representational power.
We introduce the operation of possibility qualification and show how. this modal-like operator can be used to represent "typical" or default knowledge in a theory of nonmonotonic reasoning. We investigate the representational power of this approach by looking at a number of prototypical problems from the nonmonotonic reasoning literature. In particular we look at the so called Yale shooting problem and its relation to priority in default reasoning.