{"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/differentiable-satisfiability-and","title":"Differentiable Satisfiability and Differentiable Answer Set Programming for Sampling-Based Multi-Model Optimization","arxiv_id":"1812.11948","date":"2018-12-31","proceeding":null,"authors":["Matthias Nickles"],"abstract":"We propose Differentiable Satisfiability and Differentiable Answer Set\nProgramming (Differentiable SAT/ASP) for multi-model optimization. Models\n(answer sets or satisfying truth assignments) are sampled using a novel SAT/ASP\nsolving approach which uses a gradient descent-based branching mechanism.\nSampling proceeds until the value of a user-defined multi-model cost function\nreaches a given threshold. As major use cases for our approach we propose\ndistribution-aware model sampling and expressive yet scalable probabilistic\nlogic programming. As our main algorithmic approach to Differentiable SAT/ASP,\nwe introduce an enhancement of the state-of-the-art CDNL/CDCL algorithm for\nSAT/ASP solving. Additionally, we present alternative algorithms which use an\nunmodified ASP solver (Clingo/clasp) and map the optimization task to\nconventional answer set optimization or use so-called propagators. We also\nreport on the open source software DelSAT, a recent prototype implementation of\nour main algorithm, and on initial experimental results which indicate that\nDelSATs performance is, when applied to the use case of probabilistic logic\ninference, on par with Markov Logic Network (MLN) inference performance,\ndespite having advantageous properties compared to MLNs, such as the ability to\nexpress inductive definitions and to work with probabilities as weights\ndirectly in all cases. Our experiments also indicate that our main algorithm is\nstrongly superior in terms of performance compared to the presented alternative\napproaches which reduce a common instance of the general problem to regular\nSAT/ASP.","url_abs":"http://arxiv.org/abs/1812.11948v1","url_pdf":"http://arxiv.org/pdf/1812.11948v1.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":"differentiable-satisfiability-and","repo_url":"https://github.com/MatthiasNickles/diff-SAT","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"model-optimization","task_name":"Model Optimization"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}