{"id":3342,"date":"2022-07-13T09:30:00","date_gmt":"2022-07-13T07:30:00","guid":{"rendered":"https:\/\/cirpicme.org\/?page_id=3342"},"modified":"2022-07-26T13:08:02","modified_gmt":"2022-07-26T11:08:02","slug":"reinforcement-learning-approach-for-characterizing-a-suitable-cognitive-framework-of-a-dynamic-slab-yard-control-decision-making-process","status":"publish","type":"page","link":"https:\/\/cirpicme.org\/index.php\/symposium-on-international-workshop-on-emergent-synthesis-iwes-2022\/reinforcement-learning-approach-for-characterizing-a-suitable-cognitive-framework-of-a-dynamic-slab-yard-control-decision-making-process\/","title":{"rendered":"A reinforcement learning approach for characterizing a suitable cognitive framework of a dynamic slab-yard control decision-making process"},"content":{"rendered":"\n<p class=\"wp-block-paragraph\"><em>by Hajime Mizuyama (Japan)<\/em><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Abstract<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">A slab yard upstream of a heating furnace in a steel factory is controlled by a human operator with a crane in a dynamic environment, where new slabs arrive at the yard, and heated slabs depart from the furnace stochastically. The performance of this dynamic slab-yard control decision-making process depends, at least partly, on how the operator cognizes the decision-making problem. Thus, it is essential to characterize the suitable cognitive framework of the problem not only to enhance and stabilize the performance but also to support the decision- making process effectively. This paper proposes a reinforcement learning approach built around a serious game model that mimics the production control task for this challenge. The interface aspect of the suitable cognitive framework for the task is characterized by conducting numerical experiments using the models. The experiments show that the suitable interface depends on the congestion of the yard.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Keywords<\/strong>: Dynamic decision-making process; Production-control decisions; Reinforcement learning; Serious games; Slab-yard control<\/p>\n\n\n\n<div style=\"height:20px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Video presentation<\/strong><\/p>\n\n\n\n<figure class=\"wp-block-video\"><video height=\"1080\" style=\"aspect-ratio: 1920 \/ 1080;\" width=\"1920\" controls src=\"https:\/\/cirpicme.org\/wp-content\/uploads\/2022\/07\/Hajime_Mizuyama.mp4\"><\/video><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\"><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Presenting author<\/strong><\/p>\n\n\n\n<figure class=\"wp-block-table is-style-stripes\"><table class=\"has-background\" style=\"background-color:#f3f4f5\"><tbody><tr><td><\/td><td><\/td><td><\/td><\/tr><tr><td><img data-recalc-dims=\"1\" loading=\"lazy\" decoding=\"async\" width=\"150\" height=\"150\" class=\"wp-image-3675\" style=\"width: 150px;\" src=\"https:\/\/i0.wp.com\/cirpicme.org\/wp-content\/uploads\/2022\/07\/Hajime_Mizuyama_Photo.png?resize=150%2C150&#038;ssl=1\" alt=\"\" srcset=\"https:\/\/i0.wp.com\/cirpicme.org\/wp-content\/uploads\/2022\/07\/Hajime_Mizuyama_Photo.png?w=220&amp;ssl=1 220w, https:\/\/i0.wp.com\/cirpicme.org\/wp-content\/uploads\/2022\/07\/Hajime_Mizuyama_Photo.png?resize=150%2C150&amp;ssl=1 150w\" sizes=\"auto, (max-width: 150px) 100vw, 150px\" \/><\/td><td><strong>Name:<\/strong><br><br><strong>Affiliation:<\/strong><br><br><strong>Email:<\/strong><\/td><td>Hajime Mizuyama<br><br>Aoyama Gakuin University<br><br>mizuyama@ise.aoyama.ac.jp<\/td><\/tr><tr><td><\/td><td><\/td><td><\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\"><\/p>\n","protected":false},"excerpt":{"rendered":"<p>by Hajime Mizuyama (Japan) Abstract A slab yard upstream of a heating furnace in a steel factory is controlled by a human operator with a crane in a dynamic environment, where new slabs arrive at the yard, and heated slabs depart from the furnace stochastically. The performance of this dynamic&#8230;<\/p>\n<p> <a class=\"continue-reading-link\" href=\"https:\/\/cirpicme.org\/index.php\/symposium-on-international-workshop-on-emergent-synthesis-iwes-2022\/reinforcement-learning-approach-for-characterizing-a-suitable-cognitive-framework-of-a-dynamic-slab-yard-control-decision-making-process\/\"><span>Continue reading<\/span><i class=\"crycon-right-dir\"><\/i><\/a> <\/p>\n","protected":false},"author":296,"featured_media":0,"parent":3554,"menu_order":0,"comment_status":"open","ping_status":"closed","template":"","meta":{"nf_dc_page":"","om_disable_all_campaigns":false,"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"class_list":["post-3342","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/cirpicme.org\/index.php\/wp-json\/wp\/v2\/pages\/3342","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/cirpicme.org\/index.php\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/cirpicme.org\/index.php\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/cirpicme.org\/index.php\/wp-json\/wp\/v2\/users\/296"}],"replies":[{"embeddable":true,"href":"https:\/\/cirpicme.org\/index.php\/wp-json\/wp\/v2\/comments?post=3342"}],"version-history":[{"count":3,"href":"https:\/\/cirpicme.org\/index.php\/wp-json\/wp\/v2\/pages\/3342\/revisions"}],"predecessor-version":[{"id":3833,"href":"https:\/\/cirpicme.org\/index.php\/wp-json\/wp\/v2\/pages\/3342\/revisions\/3833"}],"up":[{"embeddable":true,"href":"https:\/\/cirpicme.org\/index.php\/wp-json\/wp\/v2\/pages\/3554"}],"wp:attachment":[{"href":"https:\/\/cirpicme.org\/index.php\/wp-json\/wp\/v2\/media?parent=3342"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}