Neural Guided Constraint Logic Programming for Program Synthesis

Autor:	Zhang, Lisa, Rosenblatt, Gregory, Fetaya, Ethan, Liao, Renjie, Byrd, William E., Might, Matthew, Urtasun, Raquel, Zemel, Richard
Rok vydání:	2018
Předmět:	Computer Science - Machine Learning Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: To appear in NIPS 2018
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1809.02840 Zobrazit plný text záznamu View this record from Arxiv