Abstract
RTL is an algorithm designed to learn any number of simple, mutually dependent relations, producing recursive programs that are stratified in the sense given by Apt. In this paper, we present a revised algorithm and its implementation based on previous theoretical works that establish properties and limits of the learning framework. The algorithm is described both in abstract form and through an example. Emphasis is put on the way RTL uses induction and domain knowledge to guide the search towards specific kinds of hypothesis. The algorithm has been tested on three different domains obtaining encouraging results, as reported in the discussion. Finally, it is shown experimentally that the control strategy realized is somewhat independent of the order in which concepts are learned.
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Baroglio, C., Botta, M. (1995). Multiple predicate learning with RTL. In: Gori, M., Soda, G. (eds) Topics in Artificial Intelligence. AI*IA 1995. Lecture Notes in Computer Science, vol 992. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-60437-5_5
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DOI: https://doi.org/10.1007/3-540-60437-5_5
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