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Inductive Learning of Answer Set Programs

  • Mark Law
  • Alessandra Russo
  • Krysia Broda
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8761)

Abstract

Existing work on Inductive Logic Programming (ILP) has focused mainly on the learning of definite programs or normal logic programs. In this paper, we aim to push the computational boundary to a wider class of programs: Answer Set Programs. We propose a new paradigm for ILP that integrates existing notions of brave and cautious semantics within a unifying learning framework whose inductive solutions are Answer Set Programs and examples are partial interpretations We present an algorithm that is sound and complete with respect to our new notion of inductive solutions. We demonstrate its applicability by discussing a prototype implementation, called ILASP (Inductive Learning of Answer Set Programs), and evaluate its use in the context of planning. In particular, we show how ILASP can be used to learn agent’s knowledge about the environment. Solutions of the learned ASP program provide plans for the agent to travel through the given environment.

Keywords

Inductive Reasoning Learning Answer Set Programs Nonmonotonic Inductive Logic Programming 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Mark Law
    • 1
  • Alessandra Russo
    • 1
  • Krysia Broda
    • 1
  1. 1.Department of ComputingImperial College LondonUnited Kingdom

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