Some Properties of Plausible Reasoning
This work addresses foundational issues in knowledge representation for AI researchers, though it is incremental as it builds on prior probabilistic frameworks.
The paper tackles the challenge of unifying different forms of plausible reasoning, such as defaults and likelihood, by proposing a system based on subjective Bayesian probability, which provides approximations, semantics, and consistency results but leaves practical application unresolved.
This paper presents a plausible reasoning system to illustrate some broad issues in knowledge representation: dualities between different reasoning forms, the difficulty of unifying complementary reasoning styles, and the approximate nature of plausible reasoning. These issues have a common underlying theme: there should be an underlying belief calculus of which the many different reasoning forms are special cases, sometimes approximate. The system presented allows reasoning about defaults, likelihood, necessity and possibility in a manner similar to the earlier work of Adams. The system is based on the belief calculus of subjective Bayesian probability which itself is based on a few simple assumptions about how belief should be manipulated. Approximations, semantics, consistency and consequence results are presented for the system. While this puts these often discussed plausible reasoning forms on a probabilistic footing, useful application to practical problems remains an issue.