Abstract
Top-down learners suffer often from the plateau problem (or myopia) of their greedy search algorithms. One way to address this is to extend the top-down greedy search, which grows the clauses, with relational clichés. Using clichés the search is no longer constrained to adding one literal at a time: combinations of literals instantiating clichés are tried as well. The paper presents CLUSE: Clichés Learned and USEd, a system that learns clichés that are then used either within a domain, or across domains. CLUSE is a bottom-up learner, in which generalization proceeds according to Contextual LGG (CLGG). CLGG is an extension of LGG that takes into account the context in which a pair of literals is generalized. The paper defines CLGG, illustrates how clichés are learned, and shows that the complexity of this learning is polynomial.
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Morin, J., Matwin, S. (2000). Learning Relational Clichés with Contextual LGG. In: Raś, Z.W., Ohsuga, S. (eds) Foundations of Intelligent Systems. ISMIS 2000. Lecture Notes in Computer Science(), vol 1932. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-39963-1_29
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DOI: https://doi.org/10.1007/3-540-39963-1_29
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