A truly intelligent virtual assistant could make many new functions possible. As we envision this agent, it would have the capabilities to: Understand your world, Leverage context to inform its actions, Interact with you naturally and effectively. The Contextually Intelligent Assistants project makes progress toward the type of contextual intelligence needed for next-generation assistants. It does this by improving the state-of-the-art in understanding task intent from task descriptions; modeling key contextual signals, such as location…
You can find information about my research and recent publications on my website.
Conversational systems interact with people through language to assist, enable, or entertain. Research at Microsoft spans dialogs that use language exclusively, or in conjunctions with additional modalities like gesture; where language is spoken or in text; and in a variety of settings, such as conversational systems in apps or devices, and situated interactions in the real world. Projects Spoken Language Understanding
Established: January 1, 2009
Driver attention is a valuable commodity in maintaining driving safety. However, with the proliferation of many interactive devices that place demands on the driver's attention while driving, effectively allocating attention with the primary goal of managing driving safety presents substantial challenges. With the promise of having semi-autonomous vehicles on the road in the near future followed by a transition to fully autonomous vehicles, we are entering an exciting period of exploration and understanding how drivers adapt to unpredictable demands…
Intervention Strategies for Increasing Engagement in Crowdsourcing: Platform, Predictions, and ExperimentsAvi Segal, Ya’akov (Kobi) Gal, Ece Kamar, Eric Horvitz, Alex Bowyer, Grant Miller, in Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence (IJCAI-16), April 19, 2016,
Applying MDP Approaches For Estimating Outcome of Interaction in Collaborative Human-Computer SettingsEce Kamar, Barbara J. Grosz, in In Proceedings of Workshop on Multi-agent Sequential Decision Making in Uncertain Domains (MSDM) 2007, January 1, 2007,
September 16, 2016
Washington State University
Machine Learning Work Shop-Session 4 – Ece Kamar – “Combining Machine and Human Intelligence in Crowdsourcing”
October 26, 2012
What’s My Teammate Going To Do? Reasoning About Probable Plans For Successful Teamwork In Mixed Networks.
December 7, 2009
July 2, 2009