Abstract
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.
Research was supported by the German Research Community (DFG), grant SCHM 1239/6-1.
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Kitzelmann, E. (2009). Analytical Inductive Functional Programming. In: Hanus, M. (eds) Logic-Based Program Synthesis and Transformation. LOPSTR 2008. Lecture Notes in Computer Science, vol 5438. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00515-2_7
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