Human Allied Artificial Intelligence is coming at 10/09/2017 - 4:00pm LPSC 125 Mon, 10/09/2017 - 4:00pm
Sriraam Natarajan Associate Professor, Department of Computer Science, University of Texas Dallas Abstract: Statistical Relational Learning (SRL) Models combine the powerful formalisms of probability theory and first-order logic to handle uncertainty in large, complex problems. While they provide a very effective representation paradigm due to their succinctness and parameter sharing, efficient learning is a significant problem in these models. First, I will discuss state-of-the-art learning method based on boosting that is representation independent. Our results demonstrate that learning multiple weak models can lead to a dramatic improvement in accuracy and efficiency. One of the key attractive properties of SRL models is that they use a rich representation for modeling the domain that potentially allows for seam-less human interaction. However, in current SRL research, the human is restricted to either being a mere labeler or being an oracle who provides the entire model. I will present our recent work that allows for more reasonable human interaction where the human input is taken as “advice” and the learning algorithm combines this advice with data. Finally, I will discuss our work on soliciting advice from humans as needed that allows for seamless interactions with the human expert. Bio: Read more: http://eecs.oregonstate.edu/colloquium/human-allied-artificial-intelligence [1] [1] http://eecs.oregonstate.edu/colloquium/human-allied-artificial-intelligence
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