A Unifying Framework for Analysis and Evaluation of Inductive Programming Systems
- DOI
- 10.2991/agi.2009.16How to use a DOI?
- 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 condi- tional higher-order term rewriting as a common framework for inductive logic and inductive functional program synthe- sis. Then we characterise the several ILP systems which be- long either to the most recently researched or currently to the most powerful IP systems within this framework. In conse- quence, 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 evalu- ated and shows the strength of IGOR II.
- Copyright
- © 2009, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Martin Hofmann AU - Emanuel Kitzelmann AU - Ute Schmid PY - 2009/06 DA - 2009/06 TI - A Unifying Framework for Analysis and Evaluation of Inductive Programming Systems BT - Proceedings of the 2nd Conference on Artificial General Intelligence (2009) PB - Atlantis Press SP - 74 EP - 79 SN - 1951-6851 UR - https://doi.org/10.2991/agi.2009.16 DO - 10.2991/agi.2009.16 ID - Hofmann2009/06 ER -