A Model+Solver Approach to Concept Learning

Conference paper

DOI: 10.1007/978-3-319-49130-1_20

Part of the Lecture Notes in Computer Science book series (LNCS, volume 10037)
Cite this paper as:
Lisi F.A. (2016) A Model+Solver Approach to Concept Learning. In: Adorni G., Cagnoni S., Gori M., Maratea M. (eds) AI*IA 2016 Advances in Artificial Intelligence. AI*IA 2016. Lecture Notes in Computer Science, vol 10037. Springer, Cham

Abstract

Many Concept Learning problems can be seen as Constraint Satisfaction Problems (CSP). In this paper, we propose a model+solver approach to Concept Learning which combines the efficacy of Description Logics (DLs) in conceptual modeling with the efficiency of Answer Set Programming (ASP) solvers in dealing with CSPs.

Keywords

Concept learning Declarative modeling Description logics Answer set programming Constraint satisfaction 

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Dipartimento di InformaticaUniversità degli Studi di Bari “Aldo Moro”BariItaly

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