{"id":153049,"date":"2006-12-01T00:00:00","date_gmt":"2006-12-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/mobile-opportunistic-planning\/"},"modified":"2018-10-16T20:38:04","modified_gmt":"2018-10-17T03:38:04","slug":"mobile-opportunistic-planning","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/mobile-opportunistic-planning\/","title":{"rendered":"Mobile Opportunistic Planning: Methods and Models"},"content":{"rendered":"<div class=\"asset-content\">\n<p>We describe the construction of probabilistic user models that predict the context-sensitive cost of time, and the use of these models within a system that deliberates about adding waypoints to trips opportunistically so as to satisfy standing goals. We present details about the use of the models in a prototype named Mobile Commodities (MC). When travel is sensed via GPS, MC performs an ongoing analysis about the cost of diverging from a direct path to a destination. The system considers the location of potential waypoints that can satisfy goals, pricing of items at waypoints, and costs associated with the addi-tional time and travel required for the modified trips.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>We describe the construction of probabilistic user models that predict the context-sensitive cost of time, and the use of these models within a system that deliberates about adding waypoints to trips opportunistically so as to satisfy standing goals. We present details about the use of the models in a prototype named Mobile Commodities (MC). When [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13556],"msr-publication-type":[193718],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-153049","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-artificial-intelligence","msr-locale-en_us"],"msr_publishername":"","msr_edition":"User Modeling 2007, 11th International Conference, UM 2007, Corfu, Greece, June 25-29, 2007","msr_affiliation":"","msr_published_date":"2007-07-01","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"","msr_pages_string":"10","msr_chapter":"","msr_isbn":"","msr_journal":"","msr_volume":"","msr_number":"MSR-TR-2006-184","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":"321128","msr_publicationurl":"","msr_doi":"10.1007\/978-3-540-73078-1_26","msr_publication_uploader":[{"type":"file","title":"mobile_opportunistic_planning","viewUrl":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2006\/12\/mobile_opportunistic_planning.pdf","id":321128,"label_id":0},{"type":"doi","title":"10.1007\/978-3-540-73078-1_26","viewUrl":false,"id":false,"label_id":0}],"msr_related_uploader":"","msr_attachments":[],"msr-author-ordering":[{"type":"user_nicename","value":"horvitz","user_id":32033,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=horvitz"},{"type":"user_nicename","value":"paulkoch","user_id":33207,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=paulkoch"},{"type":"text","value":"Muru 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