{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/neural-guided-constraint-logic-programming","title":"Neural Guided Constraint Logic Programming for Program Synthesis","arxiv_id":"1809.02840","date":"2018-09-08","proceeding":"NeurIPS 2018 12","authors":["Lisa Zhang","Gregory Rosenblatt","Ethan Fetaya","Renjie Liao","William E. Byrd","Matthew Might","Raquel Urtasun","Richard Zemel"],"abstract":"Synthesizing programs using example input/outputs is a classic problem in\nartificial intelligence. We present a method for solving Programming By Example\n(PBE) problems by using a neural model to guide the search of a constraint\nlogic programming system called miniKanren. Crucially, the neural model uses\nminiKanren's internal representation as input; miniKanren represents a PBE\nproblem as recursive constraints imposed by the provided examples. We explore\nRecurrent Neural Network and Graph Neural Network models. We contribute a\nmodified miniKanren, drivable by an external agent, available at\nhttps://github.com/xuexue/neuralkanren. We show that our neural-guided approach\nusing constraints can synthesize programs faster in many cases, and\nimportantly, can generalize to larger problems.","url_abs":"http://arxiv.org/abs/1809.02840v3","url_pdf":"http://arxiv.org/pdf/1809.02840v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"neural-guided-constraint-logic-programming","repo_url":"https://github.com/xuexue/neuralkanren","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"graph-neural-network","task_name":"Graph Neural Network"},{"task_slug":"program-synthesis","task_name":"Program Synthesis"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}