Inductive functional programming using incremental program transformation
Artificial Intelligence
Term rewriting and all that
Automatic Program Construction Techniques
Automatic Program Construction Techniques
Inductive Logic Program Synthesis with DIALOGS
ILP '96 Selected Papers from the 6th International Workshop on Inductive Logic Programming
Inductive Synthesis of Functional Programs: An Explanation Based Generalization Approach
The Journal of Machine Learning Research
Learning first-order definitions of functions
Journal of Artificial Intelligence Research
Learning first-order definitions of functions
Journal of Artificial Intelligence Research
Analytical Inductive Functional Programming
Logic-Based Program Synthesis and Transformation
Data-Driven Detection of Recursive Program Schemes
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
ILP'10 Proceedings of the 20th international conference on Inductive logic programming
Inductive rule learning on the knowledge level
Cognitive Systems Research
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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 Golemand the inductive functional system MagicHaskellerwithin this framework. In consequence, we propose the inductive functional system IgorII 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 IgorII.