Learning Through Hypothesis Refinement Using Answer Set Programming

• Published in: Inductive Logic Programming Vol. 8812; pp. 31 - 46
• Main Authors: Athakravi, Duangtida, Corapi, Domenico, Broda, Krysia, Russo, Alessandra
• Format: Book Chapter
• Language: English
• Published: Germany Springer Berlin / Heidelberg 01.01.2014; Springer Berlin Heidelberg
• Series: Lecture Notes in Computer Science
• Subjects: Answer Set Programming; Artificial intelligence; Computer programming / software development; Hypothesis refinement; Mathematical theory of computation
• ISBN: 3662449226; 9783662449226
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-662-44923-3_3

Recent work has shown how a meta-level approach to inductive logic programming, which uses a semantic-preserving transformation of a learning task into an abductive reasoning problem, can address a large class of multi-predicate, nonmonotonic learning in a sound and complete manner. An Answer Set Programming (ASP) implementation, called ASPAL, has been proposed that uses ASP fixed point computation to solve a learning task, thus delegating the search to the ASP solver. Although this meta-level approach has been shown to be very general and flexible, the scalability of its ASP implementation is constrained by the grounding of the meta-theory. In this paper we build upon these results and propose a new meta-level learning approach that overcomes the scalability problem of ASPAL by breaking the learning process up into small manageable steps and using theory revision over the meta-level representation of the hypothesis space to improve the hypothesis computed at each step. We empirically evaluate the computational gain with respect to ASPAL using two different answer set solvers.