A Process Theory of Non-Monotonic Inference

Abstract

Artificial Intelligence needs a formal theory of the process of reasoning. Ideally, such a theory would decide, for every proposition and state of the process, whether the program should believe the proposition in that state, or remain agnostic. Without inference rules, nonmonotonic inferences cannot be explained in the same relational, rule-based fashion as other inferences. But with such rules, theoremhood is often formally undecidable and thus a useless criterion for our purpose. So how could any system be a non-monotonic logic programming language?. Our method uses the language, inference rules and proofs of logics, but ignores theoremhood. Instead, it defines states of the reasoning process, and focuses on current as the criterion for belief. It defines beliefs and valid proof for given states, and we prove in [5] that a belief is admissible iff it is proven. The primitive nonmonotonic condition is currently unproven. The theory, Logical Process Theory, can accept a range of logics. It was inspired by Doyle's RMS [3] and is similar to his more recent theory in [4|. A model implementation, WATSON, exists and has been used to write a small diagnostic reasoner, which reasons ally using violation of expectations and an abstraction hierarchy.