{"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/deeplogic-towards-end-to-end-differentiable","title":"DeepLogic: Towards End-to-End Differentiable Logical Reasoning","arxiv_id":"1805.07433","date":"2018-05-18","proceeding":null,"authors":["Nuri Cingillioglu","Alessandra Russo"],"abstract":"Combining machine learning with logic-based expert systems in order to get\nthe best of both worlds are becoming increasingly popular. However, to what\nextent machine learning can already learn to reason over rule-based knowledge\nis still an open problem. In this paper, we explore how symbolic logic, defined\nas logic programs at a character level, is learned to be represented in a\nhigh-dimensional vector space using RNN-based iterative neural networks to\nperform reasoning. We create a new dataset that defines 12 classes of logic\nprograms exemplifying increased level of complexity of logical reasoning and\ntrain the networks in an end-to-end fashion to learn whether a logic program\nentails a given query. We analyse how learning the inference algorithm gives\nrise to representations of atoms, literals and rules within logic programs and\nevaluate against increasing lengths of predicate and constant symbols as well\nas increasing steps of multi-hop reasoning.","url_abs":"http://arxiv.org/abs/1805.07433v3","url_pdf":"http://arxiv.org/pdf/1805.07433v3.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":"deeplogic-towards-end-to-end-differentiable","repo_url":"https://github.com/nuric/deeplogic","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"logical-reasoning","task_name":"Logical Reasoning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1805.07433","atlas_url":"https://app.syntology.ai/?focus=1805.07433","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1805.07433"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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