Satinder Singh (University of Michigan) “Deep Reinforcement Learning for Sequential Decision Making Tasks with Natural Language Interaction”

When:
September 29, 2017 @ 12:00 pm – 1:15 pm
2017-09-29T12:00:00-04:00
2017-09-29T13:15:00-04:00
Where:
Hackerman Hall B17
3400 N Charles St
Baltimore, MD 21218
USA
Cost:
Free
Contact:
Center for Language and Speech Processing

Abstract

The success of Deep Learning (DL) on visual perception has led to rapid progress on Reinforcement Learning (RL) tasks with visual inputs. More recently, Deep Learning is showing promise at certain kinds of supervised natural language problems and this too is making its way into helping on RL tasks with natural language inputs. In this talk, I will describe two projects in this direction from my group. The first (url 1 below) involves learning to query, reason, and answer questions on simple forms of ambiguous texts designed to focus on a specific problem that occurs in dialog systems. The second (url 2 below) involves zero shot generalization to unseen instructions in a 3d maze navigation task for which we develop a hierarchical DeepRL architecture. 
 
Biography

Dr. Satinder Singh is a Professor of Computer Science & Engineering at the University of Michigan where he also served as the Director of the Artificial Intelligence Laboratory from 2006 to 2017. He is also a co-founder and Chief Scientist of Cogitai, Inc.  Dr. Singh’s research interests focus on the field of Reinforcement Learning, i.e., on building algorithms, theory, and architectures for software agents that can learn how to act in uncertain, complex, and dynamic environments. Specific interests include building models of dynamical systems from time-series data, learning good interventions in human-machine interaction, dealing with partial observability and hidden state in sequential decision-making, dealing with the challenge of exploration-exploitation and delayed feedback, explaining animal and human decision making using computational models, and optimal querying in semi-autonomous agents based on value of information. He is interested in applications from healthcare, robotics, and game-playing. He is a Fellow of the Association for the Advancement of Artificial Intelligence, was Program Co-Chair of AAAI 2016, has received an outstanding faculty award from his department, and has published over 150 papers in his field.

Johns Hopkins University

Johns Hopkins University, Whiting School of Engineering

Center for Language and Speech Processing
Hackerman 226
3400 North Charles Street, Baltimore, MD 21218-2680

Center for Language and Speech Processing