Resource-bounded Relational Reasoning: Induction and Deduction Through Stochastci Matching

摘要

One of the obstacles to widely using first-order logic languages is the fact that relational inference is intractable in the worst case. This paper presents an any-time relational inference algorithm: it proceeds by stochastically sampling the inference search space, after this space has been judiciously restricted using strongly- typed logic-like declarations. We present a relational learner producing programs geared to stochastic inference, named STILL, to enforce The potentialities of this framework. STILL handles examples described as definite or constrained clauses, and Uses sampling-based heuristics again to achieve any-time learning.