Research in Machine Learning (ML) has traditionally focussed on designing effective algorithms for solving particular tasks. However, there is an increasing interest in providing the user with a means for specifying what the ML problem in hand actually is rather than letting him struggle to outline how the solution to that problem needs to be computed. This corresponds to a model+solver approach to ML, in which the user specifies the problem in a declarative modeling language and the system automatically transforms such models into a format that can be used by a solver to efficiently generate a solution. In this paper, we propose a model+solver approach to Concept Learning problems which combines the efficacy of Description Logics (DLs) in conceptual modeling with the efficiency of Answer Set Programming (ASP) solvers in dealing with constraint satisfaction problems. In particular, the approach consists of a declarative modeling language based on second-order DLs under Henkin semantics, and a mechanism for transforming second-order DL formulas into a format processable by ASP solvers.
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|Titolo:||Model with DLs + Solve with ASP: A case study from Concept Learning|
|Data di pubblicazione:||2016|
|Citazione:||Model with DLs + Solve with ASP: A case study from Concept Learning / Lisi, Francesca A. - 1645(2016), pp. 174-189. ((Intervento presentato al convegno 31st Italian Conference on Computational Logic, CILC 2016 tenutosi a Milano nel 2016.|
|Appare nelle tipologie:||4.1 Contributo in Atti di convegno|