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2018 NIPS NeurIPS 2018

Neural Guided Constraint Logic Programming for Program Synthesis

Abstract

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.

🌉 Interdisciplinary Bridge — Computer Science and Machine Learning and Mathematics & Optimization
📈 Trend Setter — Neural Networks
🧭 Keyword Pioneer — search guidance
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio

Authors

Lisa Zhang , Gregory Rosenblatt , Ethan Fetaya , Renjie Liao , William Byrd , Matthew Might , Raquel Urtasun , Richard Zemel

Topics

Machine Learning > Core Methods > Representation Learning Machine Learning > Application Areas > Knowledge Distillation Mathematics & Optimization > Optimization > Stochastic Methods Computer Science > Foundations > Algorithms Computer Science > Applications > Software Engineering Machine Learning > Core Methods > Neural Networks

Keywords

program synthesis recurrent neural network programming by example search guidance graph neural network constraint logic programming neural guided search
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