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Neural Guided Constraint Logic Programming for Program Synthesis
[article]
2018
arXiv
pre-print
Synthesizing programs using example input/outputs is a classic problem in artificial intelligence. We present a method for solving Programming By Example (PBE) problems by using a neural model to guide the search of a constraint logic programming system called miniKanren. Crucially, the neural model uses miniKanren's internal representation as input; miniKanren represents a PBE problem as recursive constraints imposed by the provided examples. We explore Recurrent Neural Network and Graph
arXiv:1809.02840v3
fatcat:npwa6kgpibgefi4vzkqc5kr4k4