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Analysis and Evaluation of Inductive Programming Systems in a Higher-Order Framework

  • Martin Hofmann
  • Emanuel Kitzelmann
  • Ute Schmid
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5243)

Abstract

In this paper we present a comparison of several inductive programming (IP) systems. IP addresses the problem of learning (recursive) programs from incomplete specifications, such as input/output examples. First, we introduce conditional higher-order term rewriting as a common framework for inductive program synthesis. Then we characterise the ILP system Golem and the inductive functional system MagicHaskeller within this framework. In consequence, we propose the inductive functional system Igor II as a powerful and efficient approach to IP. Performance of all systems on a representative set of sample problems is evaluated and shows the strength of Igor II.

Keywords

Function Symbol Inductive Logic Programming Horn Clause Restriction Bias Preference Bias 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Martin Hofmann
    • 1
  • Emanuel Kitzelmann
    • 1
  • Ute Schmid
    • 1
  1. 1.University of BambergGermany

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