{"id":1345,"date":"2023-07-10T08:27:42","date_gmt":"2023-07-10T07:27:42","guid":{"rendered":"https:\/\/babel.isa.uma.es\/kipr\/?p=1345"},"modified":"2023-07-10T08:27:42","modified_gmt":"2023-07-10T07:27:42","slug":"improving-pomdp-solving-efficiency-by-eliminating-variables-in-the-state-structure","status":"publish","type":"post","link":"https:\/\/babel.isa.uma.es\/kipr\/?p=1345","title":{"rendered":"Improving POMDP solving efficiency by eliminating variables in the state structure"},"content":{"rendered":"<h4>Eric A. Hansen, <strong>An integrated approach to solving influence diagrams and finite-horizon partially observable decision processes,<\/strong> . Artificial Intelligence, Volume 294, 2021 <a href=\"https:\/\/doi.org\/10.1016\/j.artint.2020.103431\" target=\"_blank\">DOI: 10.1016\/j.artint.2020.103431<\/a>.<\/h4>\n<blockquote><p>We show how to integrate a variable elimination approach to solving influence diagrams with a value iteration approach to solving finite-horizon partially observable Markov decision processes (POMDPs). The integration of these approaches creates a variable elimination algorithm for influence diagrams that has much more relaxed constraints on elimination order, which allows improved scalability in many cases. The new algorithm can also be viewed as a generalization of the value iteration algorithm for POMDPs that solves non-Markovian as well as Markovian problems, in addition to leveraging a factored representation for improved efficiency. The development of a single algorithm that integrates and generalizes both of these classic algorithms, one for influence diagrams and the other for POMDPs, unifies these two approaches to solving Bayesian decision problems in a way that combines their complementary advantages.<\/p><\/blockquote>\n","protected":false},"excerpt":{"rendered":"<p>Eric A. Hansen, An integrated approach to solving influence diagrams and finite-horizon partially observable decision processes, . Artificial Intelligence, Volume <span class=\"ellipsis\">&hellip;<\/span> <span class=\"more-link-wrap\"><a href=\"https:\/\/babel.isa.uma.es\/kipr\/?p=1345\" class=\"more-link\"><span>Read More &rarr;<\/span><\/a><\/span><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[37],"tags":[116,63],"class_list":["post-1345","post","type-post","status-publish","format-standard","hentry","category-artificial-intelligence","tag-pomdps","tag-task-planning"],"_links":{"self":[{"href":"https:\/\/babel.isa.uma.es\/kipr\/index.php?rest_route=\/wp\/v2\/posts\/1345"}],"collection":[{"href":"https:\/\/babel.isa.uma.es\/kipr\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/babel.isa.uma.es\/kipr\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/babel.isa.uma.es\/kipr\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/babel.isa.uma.es\/kipr\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=1345"}],"version-history":[{"count":1,"href":"https:\/\/babel.isa.uma.es\/kipr\/index.php?rest_route=\/wp\/v2\/posts\/1345\/revisions"}],"predecessor-version":[{"id":1346,"href":"https:\/\/babel.isa.uma.es\/kipr\/index.php?rest_route=\/wp\/v2\/posts\/1345\/revisions\/1346"}],"wp:attachment":[{"href":"https:\/\/babel.isa.uma.es\/kipr\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=1345"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/babel.isa.uma.es\/kipr\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=1345"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/babel.isa.uma.es\/kipr\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=1345"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}