{"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/from-gameplay-to-symbolic-reasoning-learning","title":"From Gameplay to Symbolic Reasoning: Learning SAT Solver Heuristics in the Style of Alpha(Go) Zero","arxiv_id":"1802.05340","date":"2018-02-14","proceeding":null,"authors":["Fei Wang","Tiark Rompf"],"abstract":"Despite the recent successes of deep neural networks in various fields such\nas image and speech recognition, natural language processing, and reinforcement\nlearning, we still face big challenges in bringing the power of numeric\noptimization to symbolic reasoning. Researchers have proposed different avenues\nsuch as neural machine translation for proof synthesis, vectorization of\nsymbols and expressions for representing symbolic patterns, and coupling of\nneural back-ends for dimensionality reduction with symbolic front-ends for\ndecision making. However, these initial explorations are still only point\nsolutions, and bear other shortcomings such as lack of correctness guarantees.\nIn this paper, we present our approach of casting symbolic reasoning as games,\nand directly harnessing the power of deep reinforcement learning in the style\nof Alpha(Go) Zero on symbolic problems. Using the Boolean Satisfiability (SAT)\nproblem as showcase, we demonstrate the feasibility of our method, and the\nadvantages of modularity, efficiency, and correctness guarantees.","url_abs":"http://arxiv.org/abs/1802.05340v1","url_pdf":"http://arxiv.org/pdf/1802.05340v1.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":"from-gameplay-to-symbolic-reasoning-learning","repo_url":"https://github.com/dmeoli/neuro-sat","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"decision-making","task_name":"Decision Making"},{"task_slug":"deep-reinforcement-learning","task_name":"Deep Reinforcement Learning"},{"task_slug":"dimensionality-reduction","task_name":"Dimensionality Reduction"},{"task_slug":"machine-translation","task_name":"Machine Translation"},{"task_slug":"reinforcement-learning","task_name":"Reinforcement Learning"},{"task_slug":"reinforcement-learning-1","task_name":"Reinforcement Learning (RL)"},{"task_slug":"speech-recognition","task_name":"Speech Recognition"},{"task_slug":"translation","task_name":"Translation"},{"task_slug":"reinforcement-learning-2","task_name":"reinforcement-learning"},{"task_slug":"speech-recognition-1","task_name":"speech-recognition"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}