{"id":794018,"date":"2021-11-16T08:00:33","date_gmt":"2021-11-16T16:00:33","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&#038;p=794018"},"modified":"2021-11-09T11:52:30","modified_gmt":"2021-11-09T19:52:30","slug":"research-talk-evaluating-human-like-navigation-in-3d-video-games","status":"publish","type":"msr-video","link":"https:\/\/www.microsoft.com\/en-us\/research\/video\/research-talk-evaluating-human-like-navigation-in-3d-video-games\/","title":{"rendered":"Research talk: Evaluating human-like navigation in 3D video games"},"content":{"rendered":"<p>On the path to developing agents that learn complex human-like behavior, a key challenge is the need to quickly and accurately quantify human-likeness. While human assessments of such behavior can be highly accurate, speed and scalability are limited. The researchers address these limitations through a novel automated Navigation Turing Test (NTT) that learns to predict human judgments of human-likeness. They demonstrate the effectiveness of their automated NTT on a navigation task in a complex 3D environment. They investigated six classification models to shed light on the types of architectures best suited to this task, and they validated them against data collected through a human NTT. The best models achieve high accuracy when distinguishing true human and agent behavior. At the same time, the researchers show that predicting finer-grained human assessment of agents\u2019 progress towards human-like behavior remains unsolved. Their work takes an important step towards agents that more effectively learn complex human-like behavior.<\/p>\n<p>Learn more about the 2021 Microsoft Research Summit: <a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"https:\/\/Aka.ms\/researchsummit\">https:\/\/Aka.ms\/researchsummit<span class=\"sr-only\"> (opens in new tab)<\/span><\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>On the path to developing agents that learn complex human-like behavior, a key challenge is the need to quickly and accurately quantify human-likeness. While human assessments of such behavior can be highly accurate, speed and scalability are limited. The researchers address these limitations through a novel automated Navigation Turing Test (NTT) that learns to predict [&hellip;]<\/p>\n","protected":false},"featured_media":794021,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","footnotes":""},"research-area":[13556],"msr-video-type":[261263,261311],"msr-locale":[268875],"msr-post-option":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-794018","msr-video","type-msr-video","status-publish","has-post-thumbnail","hentry","msr-research-area-artificial-intelligence","msr-video-type-research-summit-2021","msr-video-type-reinforcement-learning","msr-locale-en_us"],"msr_download_urls":"","msr_external_url":"https:\/\/youtu.be\/k074ka2UM5U","msr_secondary_video_url":"","msr_video_file":"","_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video\/794018"}],"collection":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video"}],"about":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/types\/msr-video"}],"version-history":[{"count":1,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video\/794018\/revisions"}],"predecessor-version":[{"id":794024,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video\/794018\/revisions\/794024"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media\/794021"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=794018"}],"wp:term":[{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=794018"},{"taxonomy":"msr-video-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video-type?post=794018"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=794018"},{"taxonomy":"msr-post-option","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-post-option?post=794018"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=794018"},{"taxonomy":"msr-pillar","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-pillar?post=794018"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}