Nonmonotonic Inferences with Qualitative Conditionals based on Preferred Structures on Worlds
This work addresses inference challenges in AI and knowledge representation, offering a more robust and efficient method for handling conditional knowledge bases, though it appears incremental as it builds on prior systems.
The paper tackles the problem of nonmonotonic inference with qualitative conditionals by introducing a new inference relation called system W, which avoids the drowning problem and extends existing methods like system Z and skeptical c-inference while maintaining tractability.
A conditional knowledge base R is a set of conditionals of the form "If A, the usually B". Using structural information derived from the conditionals in R, we introduce the preferred structure relation on worlds. The preferred structure relation is the core ingredient of a new inference relation called system W inference that inductively completes the knowledge given explicitly in R. We show that system W exhibits desirable inference properties like satisfying system P and avoiding, in contrast to e.g. system Z, the drowning problem. It fully captures and strictly extends both system Z and skeptical c-inference. In contrast to skeptical c-inference, it does not require to solve a complex constraint satisfaction problem, but is as tractable as system Z.