Sub-unification: a tool for efficient induction of recursive programs
ML92 Proceedings of the ninth international workshop on Machine learning
Inductive functional programming using incremental program transformation
Artificial Intelligence
A Methodology for LISP Program Construction from Examples
Journal of the ACM (JACM)
Machine Learning
Inductive Logic Programming: From Machine Learning to Software Engineering
Inductive Logic Programming: From Machine Learning to Software Engineering
ECML '93 Proceedings of the European Conference on Machine Learning
Inductive Synthesis of Functional Programs: An Explanation Based Generalization Approach
The Journal of Machine Learning Research
Analysis and Evaluation of Inductive Programming Systems in a Higher-Order Framework
KI '08 Proceedings of the 31st annual German conference on Advances in Artificial Intelligence
RTA'03 Proceedings of the 14th international conference on Rewriting techniques and applications
Data-Driven Detection of Recursive Program Schemes
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
Concept of inductive programming supporting anthropomorphic information technology
Journal of Computer and Systems Sciences International
Inductive rule learning on the knowledge level
Cognitive Systems Research
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We describe a new method to induce functional programs from small sets of non-recursive equations representing a subset of their input-output behaviour. Classical attempts to construct functional Lisp programs from input/output-examples are analytical , i.e., a Lisp program belonging to a strongly restricted program class is algorithmically derived from examples. More recent approaches enumerate candidate programs and only test them against the examples until a program which correctly computes the examples is found. Theoretically, large program classes can be induced generate-and-test based, yet this approach suffers from combinatorial explosion. We propose a combination of search and analytical techniques. The method described in this paper is search based in order to avoid strong a-priori restrictions as imposed by the classical analytical approach. Yet candidate programs are computed based on analytical techniques from the examples instead of being generated independently from the examples. A prototypical implementation shows first that programs are inducible which are not in scope of classical purely analytical techniques and second that the induction times are shorter than in recent generate-and-test based methods.