Data structures and program transformation
Science of Computer Programming
Inductive functional programming using incremental program transformation
Artificial Intelligence
A Methodology for LISP Program Construction from Examples
Journal of the ACM (JACM)
Machine Learning
Automatic Program Construction Techniques
Automatic Program Construction Techniques
Functional Programming with Bananas, Lenses, Envelopes and Barbed Wire
Proceedings of the 5th ACM Conference on Functional Programming Languages and Computer Architecture
Inductive Logic Program Synthesis with DIALOGS
ILP '96 Selected Papers from the 6th International Workshop on Inductive Logic Programming
A tutorial on the universality and expressiveness of fold
Journal of Functional Programming
Inductive Synthesis of Functional Programs: An Explanation Based Generalization Approach
The Journal of Machine Learning Research
Automated Construction of XSL-Templates
Automated Construction of XSL-Templates
Genetic programming with polymorphic types and higher-order functions
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Learning first-order definitions of functions
Journal of Artificial Intelligence Research
Learning first-order definitions of functions
Journal of Artificial Intelligence Research
IGOR2 - an analytical inductive functional programming system: tool demo
Proceedings of the 2010 ACM SIGPLAN workshop on Partial evaluation and program manipulation
An analytical inductive functional programming system that avoids unintended programs
PEPM '12 Proceedings of the ACM SIGPLAN 2012 workshop on Partial evaluation and program manipulation
Inductive rule learning on the knowledge level
Cognitive Systems Research
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Inductive programming (IP), usually defined as a search in a space of candidate programs, is an inherent exponentially complex problem. To constrain the search space, program templates have ever been one of the first choices. In previous approaches to incorporate program schemes, either an (often very well) informed expert user has to provide a template in advance, or templates are used simply on suspicion, regardless whether they are target-aiming or not. Instead of rather fit the data to the template, we present an approach to fit a template to the data. We propose to utilise universal properties of higher-order functions to detect the appropriateness of a certain template in the input/output examples. We use this technique to introduce catamorphisms on lists in our IP system Igor2.