[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-83329-en":3,"doc-seo-83329-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},83329,1374391974585,"Genevieve","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Answer Set Programming Energised End-to-End Neurosymbolic Reasoning and Learning with ASP and Energy Based Models","A general neurosymbolic reasoning and learning methodology integrates Answer Set Programming (ASP) with an energy-based model substrate. Joint optimisation operates in a continuous latent space using explicit ASP declarative semantics, fully capturing background knowledge, constraints, and non-monotonic inference. The approach generalises links between answer sets, probabilistic logic, and ASP modulo theories, offering an ASP-centric platform for robust end-to-end training in dynamic, perception- and interaction-driven domains, with practical MNIST use and evaluation on Clevr and MOT benchmarks.","arXiv :2607 .08 136v 1 [ cs .AI] 9 Jul 2026  \nAnswer Set Programming Energised!  \nEnd-to-End Neurosymbolic Reasoning and Learning with ASP and Energy Based Models  \nJakob Suchan 1 ,3 , Julius Monsen2 ,3 , Salim Baloch 1 ,3 , and Mehul Bhatt2 ,3  \n1 Constructor University Bremen, Bremen, Germany  \n[jsuchan@constructor.university](jsuchan@constructor.university)  \n2 Örebro University, Örebro, Sweden  \n[info@codesign-lab.org](info@codesign-lab.org)  \n3 CoDesign Lab > Cognitive Vision  \n[codesign-lab.org/cognitive-vision](codesign-lab.org/cognitive-vision)  \nAbstract. We present a general neurosymbolic reasoning and learning methodology based on a modular integration of answer set programming with an energy based model substrate. Key contributions are: (1) supporting joint optimisation in the continuous latent space through explicit ASP-based declarative semantics fully incorporating background knowledge, constraints, non-monotonic inference; and (2) advancing recent works at the interface of answer sets, probabilistic logic, and answer set modulo theories by providing a generalised model and practical platform for ASP-centric robust, end-to-end training for applications in dynamic domains (e.g., involving perception and interaction) . We provide a practical implementation, and demonstrate basic use and application (with MNIST), and evaluate with the visual question-answering benchmark Clevr and the multi-object tracking benchmark MOT.  \n1 Motivation  \nThe integration of machine learning and reasoning remains a challenge and an area of high interest in AI research [9] . Addressing this is particularly needed for real-world problems –e.g., in embodied perception, control, decision-making– involving interactive dynamics, uncertainty & partial observability, abnormalities etc [3,22] . Towards this, the broader purpose of this research is on high-level, semantically-guided learning of high-dimensional sensory structure keeping in mind trustworthy design. i.e., explainability, interpretability, formal verification– aimed at ethico-legal compliance vis-a-vis emerging AI regulation [1,8 , 18] . The main scientific aim is to advance systematic, robust methodological developments aimed at integrating the declaratively modelled semantic structure of problem spaces with quantitative optimisation and learning.  \nDeclarative Neurosymbolism, End-to-End. Neurosymbolic integrations from the viewpoint of KR and ML research have gained traction in recent years. Particularly relevant to the scope addressed here are declarative methodology  \n2 Suchan et al.  \ncentric integrations involving stable model semantics rooted Answer Set Programming (ASP) [4], and ASP derivatives such as specialised Answer Set Modulo Theories (ASPMT) [2,26] and Probabilistic ASP [15] . Most recently, an active line of work building upon such fundamental perspectives pertains integration of KR/ASP and (deep learning driven) computer vision aimed at realising neurosymbolic visual commonsense [22,21 ,24 ,27 ,25 ,6 , 19] . Here, the use of logic and answer set programming stands out as a sustained line of inquiry from viewpoints such as (semantic) visual-question answering [21,6 , 19], and non-monotonic visual abduction for (neurosymbolic) spatio-temporal belief maintenance in dynamic domains [25], and “in-the-wild” neurosymbolic reasoning about embodied interaction [23] . Differences in the technical framing and supported computational capabilities in these works notwithstanding, the general underlying motivation remains unified: integrating neurally-driven visual processing capabilities (to extract geometric scene elements and visual features from imagery) with highlevel, expressive conceptual commonsense knowledge with the aim to support neurosymbolic interpretation of either static and/or dynamic stimuli.  \nWith these methodologies, a caveat is that they preserve a conceptual and computational separation between symbolic inference and subsymbolic learning: neural outputs are","cbCair3VaMNpfXYK","https://ap.wps.com/l/cbCair3VaMNpfXYK","pdf",2360850,3,1,19,"English","en",105,"# Abstract\n# Motivation\n# Declarative Neurosymbolism, End-to-End\n# ASPEn – ASP Energised","[{\"question\":\"What does ASPEn propose for neurosymbolic reasoning and learning?\",\"answer\":\"ASPEn proposes an integration of Answer Set Programming with energy-based models, interpreting ASP world models as structured embeddings in a continuous optimisation landscape to enable end-to-end training.\"},{\"question\":\"How does the method support joint optimisation beyond purely symbolic inference?\",\"answer\":\"It uses explicit ASP-based declarative semantics while performing optimisation in a continuous latent space, so symbolic constraints and non-monotonic inference influence learning directly rather than acting as a post-hoc filter.\"},{\"question\":\"Which benchmarks and example tasks are used to demonstrate the approach?\",\"answer\":\"The paper demonstrates basic usage with MNIST and evaluates on the visual question-answering benchmark Clevr and the multi-object tracking benchmark MOT.\"}]",1784186759,48,{"code":4,"msg":31,"data":32},"ok",{"site_id":25,"language":24,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"answer-set-programming-energised-end-to-end-neurosymbolic-reasoning-and-learning-with-asp-and-energy-based-models","",{"@graph":36,"@context":85},[37,53,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,50],{"item":41,"name":42,"@type":43,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":20},"https://docshare.wps.com/document/research-report/",{"item":51,"name":13,"@type":43,"position":52},"https://docshare.wps.com/document/answer-set-programming-energised-end-to-end-neurosymbolic-reasoning-and-learning-with-asp-and-energy-based-models/83329/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":24,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-26","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What 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