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Neural Guided Constraint Logic Programming for Program Synthesis
Lisa Zhang · Gregory Rosenblatt · Ethan Fetaya · Renjie Liao · William Byrd · Matthew Might · Raquel Urtasun · Richard Zemel

Tue Dec 04 07:45 AM -- 09:45 AM (PST) @ Room 210 #98

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 Neural Network models. We contribute a modified miniKanren, drivable by an external agent, available at https://github.com/xuexue/neuralkanren. We show that our neural-guided approach using constraints can synthesize programs faster in many cases, and importantly, can generalize to larger problems.

Author Information

Lisa Zhang (University of Toronto)
Gregory Rosenblatt (University of Alabama at Birmingham)
Ethan Fetaya (University of Toronto)
Renjie Liao (University of Toronto)
William Byrd (University of Alabama at Birmingham)
Matthew Might (University of Alabama at Birmingham)
Raquel Urtasun (University of Toronto)
Richard Zemel (Vector Institute/University of Toronto)

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