Abstract
One of the main problems in using logic for solving problems is the high computational costs involved in inference. In this paper, we propose the use of a notion of relevance in order to cut the search space for a solution. Instead of trying to infer a formula α directly from a large knowledge base K, we consider first only the most relevant sentences in K for the proof. If those are not enough, the set can be increased until, at the worst case, we consider the whole base K.
We show how to define a notion of relevance for first-order logic with equality and analyze the results of implementing the method and testing it over more than 700 problems from the TPTP problem library.
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Riani, J., Wassermann, R. (2004). Using Relevance to Speed Up Inference. In: Bazzan, A.L.C., Labidi, S. (eds) Advances in Artificial Intelligence – SBIA 2004. SBIA 2004. Lecture Notes in Computer Science(), vol 3171. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28645-5_3
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DOI: https://doi.org/10.1007/978-3-540-28645-5_3
Publisher Name: Springer, Berlin, Heidelberg
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