[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-665378-105":59,"doc-detail-665378-en":130},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","dynamic-programming-in-influence-diagrams-with-decision-circuits-read-online-free","Dynamic programming in influence diagrams with decision circuits - read online free","","Decision circuits provide efficient evaluation of influence diagrams by extending arithmetic circuits used for belief-network inference. The paper develops more compact decision circuits for dynamic programming in influence diagrams that use separable value functions and conditionally independent subproblems. After construction from the diagram’s global graph structure, the circuits are compiled to exploit local structure, enabling efficient evaluation together with sensitivity analysis.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/dynamic-programming-in-influence-diagrams-with-decision-circuits-read-online-free/665378/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/dynamic-programming-in-influence-diagrams-with-decision-circuits-read-online-free/665378.png","ImageObject",300,407,{"name":92,"@type":93},"asda1da","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-30","2026-10-29",true,{"@type":102,"interactionType":103,"userInteractionCount":19},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What problem does the paper address using decision circuits?","Question",{"text":112,"@type":113},"It addresses efficient evaluation and sensitivity analysis for dynamic, sequential decision problems represented by influence diagrams, extending the idea of arithmetic circuits.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How does the paper improve decision circuits for dynamic programming?",{"text":117,"@type":113},"It constructs more compact decision circuits by leveraging separable value functions and conditionally independent subproblems in influence diagrams.",{"name":119,"@type":110,"acceptedAnswer":120},"What does compilation after construction enable?",{"text":121,"@type":113},"Once the decision circuit is built from the diagram’s global structure, compilation exploits local structure to make evaluation and sensitivity analysis more efficient.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},665378,1793301842,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":19,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":39,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":129,"read_time":46},3985747861852,"https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d","Dynamic programming in in􀀍uence diagrams with decision circuits  \nRoss D. Shachter  \nManagement Science and Engineering Stanford University Stanford, CA 94305, USA [shachter@stanford.edu](shachter@stanford.edu)  \nDebarun Bhattacharjya  \nBusiness Analytics and Math Sciences IBM T. J. Watson Research Center Yorktown Heights, NY 10598, USA [debarunb@us.ibm.com](debarunb@us.ibm.com)  \nAbstract  \nDecision circuits perform e􀀎cient evaluation of in􀀍uence diagrams, building on the advances in arithmetic circuits for belief network inference [Darwiche, 2003; Bhattacharjya and Shachter, 2007] . We show how even more compact decision circuits can be constructed for dynamic programming in in􀀍uence diagrams with separable value functions and conditionally independent subproblems.  \nOnce a decision circuit has been constructed based on the diagram's \\global\" graphical structure, it can be compiled to exploit \\local\" structure for e􀀎cient evaluation and sensitivity analysis.  \n1 INTRODUCTION  \nIn􀀍uence diagrams [Howard and Matheson, 1984] are popular graphical models for representing, evaluating and analyzing a single rational decision maker's decision problem. We can exploit any separable value functions in in􀀍uence diagrams for more e􀀎cient evaluation [Tatman and Shachter, 1990; Luque and Diez, 2009] . Decision circuits are graphical representations for e􀀎cient evaluation and sensitivity analysis on in-􀀍uence diagrams with a single value node [Bhattacharjya and Shachter, 2007, 2008, 2010] . In this paper, we show how they can be applied to in􀀍uence diagrams with multiple value functions using join tree methods [Shachter and Peot, 1992; Dittmer and Jensen, 1994] .  \nArithmetic circuits [Darwiche, 2003] are graphical representations for probabilistic inference and sensitivity analysis in Bayesian belief networks. Decision circuits are a natural extension to arithmetic circuits for evaluating sequential decision problems represented as in-􀀍uence diagrams.  \nGlobal structure refers to the topology of the graph,  \nthe size and the treewidth for a given task. Techniques have been developed to exploit global structure in the construction of arithmetic circuits applying logic [Darwiche, 2002; Chavira and Darwiche, 2005; Chavira, 2007] and join trees [Park and Darwiche, 2004] . We apply join tree methods, as well, but the arithmetic circuit approaches do not deal with the additional vertex elimination order restrictions in decision problems and complex accounting for separable value functions. To address these issues, we introduce a \\branching operator\" for decision circuits, use directed chordal graphs to develop our join tree structure, and introduce decision circuit backbones as a compact representation for symmetric decision circuits.  \nLocal structure in belief networks refers to the speci􀀌c numbers, particularly zeros, in the conditional probability tables (CPTs) . Like arithmetic circuits, decision circuits are compiled so that subsequent evaluation and analysis can be more e􀀎cient. This is particularly useful for real-time decision making.  \nThis paper is organized as follows. In Section 2 wereview in􀀍uence diagrams and introduce some examples. In Section 3 we review decision circuits, and introduce a new type of decision circuit operator for branching. In Section 4 we show how to construct a decision circuit for an in􀀍uence diagram with multiple value nodes, based on directed chordal graphs and introducing an intermediate structure we call a \\decision circuit backbone.\" In Section 5 we discuss methods to compile the decision circuit, taking advantage of local structure. Finally, we conclude the paper in Section 6 with some suggestions for future research.  \n2 INFLUENCE DIAGRAMS  \nIn this section we review in􀀍uence diagrams and introduce the notation and two examples that we will refer to throughout the paper.  \nAn in􀀍uence diagram is a directed acyclic graphical  \nmodel. The nodes correspond to the variables, uncertainties drawn as ovals, ","cbCaitUrHMIWDF8l","https://ap.wps.com/l/cbCaitUrHMIWDF8l","pdf",235200,"English","# Introduction\n# Influence Diagrams\n## Notation and Examples\n## Value and Decision Modeling","[{\"question\":\"What problem does the paper address using decision circuits?\",\"answer\":\"It addresses efficient evaluation and sensitivity analysis for dynamic, sequential decision problems represented by influence diagrams, extending the idea of arithmetic circuits.\"},{\"question\":\"How does the paper improve decision circuits for dynamic programming?\",\"answer\":\"It constructs more compact decision circuits by leveraging separable value functions and conditionally independent subproblems in influence diagrams.\"},{\"question\":\"What does compilation after construction enable?\",\"answer\":\"Once the decision circuit is built from the diagram’s global structure, compilation exploits local structure to make evaluation and sensitivity analysis more efficient.\"}]","Dynamic programming in influence diagrams with decision circuits - read online free | PDF"]