Physically situated, multimodal interactive systems must often grapple with uncertainties about properties of the world, people, and their intentions and actions.\u00a0 We present methods for estimating and communicating about different uncertainties in situated interaction, leveraging the affordances of an embodied conversational agent. The approach harnesses a representation that captures both the magnitude and the sources of uncertainty, and a set of policies that select and coordinate the production of nonverbal and verbal behaviors to communicate the system\u2019s uncertainties to conversational participants. The methods are designed to enlist participants\u2019 help in a natural manner to resolve uncertainties arising during interactions. We report on a preliminary implementation of the proposed methods in a deployed system and illustrate the functionality with a trace from a sample interaction.<\/p>\n","protected":false},"excerpt":{"rendered":"
Physically situated, multimodal interactive systems must often grapple with uncertainties about properties of the world, people, and their intentions and actions.\u00a0 We present methods for estimating and communicating about different uncertainties in situated interaction, leveraging the affordances of an embodied conversational agent. The approach harnesses a representation that captures both the magnitude and the sources […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13561,13556,13555],"msr-publication-type":[193716],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-325871","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-algorithms","msr-research-area-artificial-intelligence","msr-research-area-search-information-retrieval","msr-locale-en_us"],"msr_publishername":"","msr_edition":"","msr_affiliation":"","msr_published_date":"2014-11-12","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"","msr_pages_string":"","msr_chapter":"","msr_isbn":"","msr_journal":"","msr_volume":"","msr_number":"","msr_editors":"","msr_series":"","msr_issue":"","msr_organization":"","msr_how_published":"","msr_notes":"","msr_highlight_text":"","msr_release_tracker_id":"","msr_original_fields_of_study":"","msr_download_urls":"","msr_external_url":"","msr_secondary_video_url":"","msr_longbiography":"","msr_microsoftintellectualproperty":1,"msr_main_download":"325874","msr_publicationurl":"","msr_doi":"","msr_publication_uploader":[{"type":"file","viewUrl":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2016\/11\/icmi2014-uncert.pdf","id":"325874","title":"icmi2014-uncert","label_id":"243109","label":0},{"type":"doi","viewUrl":"false","id":"false","title":"10.1145\/2663204.2663249","label_id":"243109","label":0}],"msr_related_uploader":"","msr_attachments":[],"msr-author-ordering":[{"type":"text","value":"Tomislav Pejsa","user_id":0,"rest_url":false},{"type":"user_nicename","value":"Dan Bohus","user_id":31581,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Dan Bohus"},{"type":"user_nicename","value":"Michael Cohen","user_id":32862,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Michael Cohen"},{"type":"user_nicename","value":"Nick Saw","user_id":31405,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Nick Saw"},{"type":"text","value":"James Mahoney","user_id":0,"rest_url":false},{"type":"user_nicename","value":"Eric Horvitz","user_id":32033,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Eric Horvitz"}],"msr_impact_theme":[],"msr_research_lab":[],"msr_event":[],"msr_group":[144633],"msr_project":[390800],"publication":[],"video":[],"download":[],"msr_publication_type":"inproceedings","related_content":{"projects":[{"ID":390800,"post_title":"Situated Interaction","post_name":"situated-interaction","post_type":"msr-project","post_date":"2017-07-07 12:00:28","post_modified":"2021-04-06 15:07:38","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/situated-interaction\/","post_excerpt":"The situated interaction research effort aims to enable computers to reason more deeply about their surroundings, and engage in fluid interaction with humans in physically situated settings. When people interact with each other, they engage in a rich, highly coordinated, mixed-initiative process, regulated through both verbal and non-verbal channels. In contrast, while their perceptual abilities are improving, computers are still unaware of their physical surroundings and of the \u201cphysics\u201d of human interaction. 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