Resource-bounded relational reasoning: induction and deduction through stochastic matching

Abstract : 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. Controlling both the construction and the exploitation of logic programs yields robust relational reasoning, where deductive biases are compensated for by inductive biases, and vice versa.
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Michèle Sebag, Céline Rouveirol. Resource-bounded relational reasoning: induction and deduction through stochastic matching. Machine Learning, Springer Verlag, 2000, 38 (1-2), pp.41-62. ⟨10.1023/A:1007629922420⟩. ⟨hal-00111312⟩

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