Abstract
There are two main issues to consider in an inductive learning system. These are 1) its search through the hypothesis space and 2) the amount of provided information for the system to work. In this paper we use a constrained relative least-general-generalisation (RLGG) algorithm as method of generalisation to organise the search space and an automatic example generator to reduce the user's intervention and guide the learning process. Some initial results to learn a restricted form of Horn clause concepts in chess are presented. The main limitations of the learning system and the example generator are pointed out and conclusions and future research directions indicated.
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© 1991 Springer-Verlag Berlin Heidelberg
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Morales, E. (1991). Learning features by experimentation in chess. In: Kodratoff, Y. (eds) Machine Learning — EWSL-91. EWSL 1991. Lecture Notes in Computer Science, vol 482. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0017040
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DOI: https://doi.org/10.1007/BFb0017040
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