Thesis Defense


"Algorithms for the Personalization of AI for Robots and the Smart Home"

Stephen Brawner

Thursday, May 3, 2018 at 11:30 A.M.

Room 269 (CIT 2nd Floor)

Just as an interconnected-computerized world has produced large amounts of data resulting in exciting challenges for machine learning, connected households with robots and smart devices will provide developers with an opportunity to build technologies that learn from personalized household data. However, there exists a dilemma. When limited data is available for a user, for example when they initially procure a new smart device or robot, there will be a substantial burden placed on that user to personalize it to their household by the learner. At the outset, applying predictions learned from a general population to a user will provide better predictive success. But as the amount of data provided by the user increases, intelligent methods should choose predictions more heavily weighted by the individuals examples.

We investigated three different problems to find algorithms that learn from both the general population and specialize to the human individual. We developed a solution to reduce the interactive burden when telling a robot how to organize a kitchen. Applying a context-aware recommender system improved the performance for limited user examples. Also, using the paradigm of trigger-action programming made popular by IFTTT, we sought to improve the programming experience by learning to predict the creation of programs from the user's history. Finally we developed several methods to personalize grounding natural language to trigger-action programs. In a smart home where a user can describe to an intelligent home automated system rules or programs they desire to be created, their utterances are highly context dependent. Multiple users may use similar utterances to mean different things. We present several methods that personalize the machine translation of these utterances to smart home programs.

This work presents several problems that show that learning algorithms that learn from both a general population and from personalized interactions will perform better than either learning approach alone.

Host: Professor Michael Littman