The ILASP system for Inductive Learning of Answer Set Programs
This work addresses the need for more expressive program learning in AI, enabling applications such as non-deterministic theories and defaults, though it appears incremental in its system updates.
The ILASP system tackles the problem of learning expressive Answer Set Programs, including rules and constraints, which broadens the applicability of Inductive Logic Programming to areas like preference learning and common-sense knowledge. It provides a comprehensive overview of the system's evolution, focusing on scalability improvements.
The goal of Inductive Logic Programming (ILP) is to learn a program that explains a set of examples in the context of some pre-existing background knowledge. Until recently, most research on ILP targeted learning Prolog programs. Our own ILASP system instead learns Answer Set Programs, including normal rules, choice rules and hard and weak constraints. Learning such expressive programs widens the applicability of ILP considerably; for example, enabling preference learning, learning common-sense knowledge, including defaults and exceptions, and learning non-deterministic theories. In this paper, we first give a general overview of ILASP's learning framework and its capabilities. This is followed by a comprehensive summary of the evolution of the ILASP system, presenting the strengths and weaknesses of each version, with a particular emphasis on scalability.