Learning Through Hypothesis Refinement Using Answer Set Programming

摘要

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.