Colloquium Details
Efficient Decision-Theoretic Assistance Through Relational Hierarchical Models
| Author: | Sriraam Natarajan University of Wisconsin-Madison |
|---|---|
| Date: | December 21, 2009 |
| Time: | 11:00 |
| Location: | 220 Deschutes |
| Host: | Daniel Lowd |
Abstract
Building intelligent computer assistants has been a long-cherished goal of AI. Many intelligent assistant systems were built and fine-tuned to specific application domains. In this talk, I present a general model of assistance that combines three powerful ideas: decision theory, hierarchical task models and probabilistic relational languages. I use the principles of decision theory to model the general problem of intelligent assistance and use a combination of hierarchical task models and probabilistic relational languages to specify prior knowledge of the computer assistant. The assistant exploits its prior knowledge to infer the user's goals and takes actions to assist the user. I present the results of the decision-theoretic model and its relational hierarchical extension in several domains.
Biography
Sriraam Natarajan is currently a Post-Doctoral Research Associate at the Department of Computer Science at University of Wisconsin-Madison. He graduated with his PhD from Oregon State University working with Dr. Prasad Tadepalli. His research interests lie in the field of Artificial Intelligence, with emphasis on Machine Learning, Statistical Relational Learning, Bio-Medical Applications, Reinforcement Learning, Graphical Models and Natural Language Processing. He is a co-organizer of the Statistical Relational AI Workshop at AAAI-2010.