[Theory] REMINDER: 3/14 Distinguished Lecture Series: Raymond J. Mooney, University of Texas at Austin

[Mary Marre]

*TTIC Distinguished Lecture Series:**Raymond J. Mooney,
<https://www.cs.utexas.edu/~mooney/> **University of Texas at Austin *
*Monday**, March 14, 2022 at 11:30 am CT*
[image: image.png]
*Where:    *Talk will be given *live, in-person*

                TTIC, 6045 S. Kenwood Avenue

                5th Floor, Room 530

*Virtually:* *register in advance here*
<https://uchicagogroup.zoom.us/webinar/register/WN_O8pTNeswQwywyWNPD_q_9A>



*Raymond J. Mooney*

*Professor, Department of Computer Science **and **Director of the
Artificial Intelligence Laboratory
<https://www.cs.utexas.edu/users/ai-lab/> at the University of Texas at
Austin*


------------------------------


Title: Dialog with Robots: Perceptually Grounded Communication with
Lifelong Learning

Abstract: Developing robots that can accept instructions from and
collaborate with human users is greatly enhanced by an ability to engage in
natural language dialog. Unlike most other dialog scenarios, this requires
grounding the semantic analysis of language in perception and action in the
world. Although deep-learning has greatly enhanced methods for such
grounded language understanding, it is difficult to ensure that the data
used to train such models covers all of the concepts that a robot might
encounter in practice. Therefore, we have developed methods that can
continue to learn from dialog with users during ordinary use by acquiring
additional targeted training data from the responses to intentionally
designed clarification and active learning queries. These methods use
reinforcement learning to automatically acquire dialog strategies that
support both effective immediate task completion as well as learning that
improves future performance. Using both experiments in simulation and with
real robots, we have demonstrated that these methods exhibit life-long
learning that improves long-term performance.

Bio: Raymond J. Mooney is a Professor in the Department of Computer Science
at the University of Texas at Austin. He received his Ph.D. in 1988 from
the University of Illinois at Urbana/Champaign. He is an author of over 180
published research papers, primarily in the areas of machine learning and
natural language processing. He was the President of the International
Machine Learning Society from 2008-2011, program co-chair for AAAI 2006,
general chair for HLT-EMNLP 2005, and co-chair for ICML 1990. He is a
Fellow of AAAI, ACM, and ACL and the recipient of the Classic Paper award
from AAAI-19 and best paper awards from AAAI-96, KDD-04, ICML-05 and
ACL-07.

*Host:* *David McAllester* <mcallester at ttic.edu>



[image: image.png]




Mary C. Marre
Faculty Administrative Support
*Toyota Technological Institute*
*6045 S. Kenwood Avenue*
*Chicago, IL  60637*
*mmarre at ttic.edu <mmarre at ttic.edu>*


On Fri, Mar 4, 2022 at 1:18 PM Mary Marre <mmarre at ttic.edu> wrote:

> *TTIC Distinguished Lecture Series:**Raymond J. Mooney,
> <https://www.cs.utexas.edu/~mooney/> **University of Texas at Austin *
> *Monday**, March 14, 2022 at 11:30 am CT*
> [image: image.png]
> *Where:    *Talk will be given *live, in-person*
>
>                 TTIC, 6045 S. Kenwood Avenue
>
>                 5th Floor, Room 530
>
> *Virtually:* *register in advance here*
> <https://uchicagogroup.zoom.us/webinar/register/WN_O8pTNeswQwywyWNPD_q_9A>
>
>
>
> *Raymond J. Mooney*
>
> *Professor, Department of Computer Science **and **Director of the
> Artificial Intelligence Laboratory
> <https://www.cs.utexas.edu/users/ai-lab/> at the University of Texas at
> Austin*
>
>
> ------------------------------
>
>
> Title: Dialog with Robots: Perceptually Grounded Communication with
> Lifelong Learning
>
> Abstract: Developing robots that can accept instructions from and
> collaborate with human users is greatly enhanced by an ability to engage in
> natural language dialog. Unlike most other dialog scenarios, this requires
> grounding the semantic analysis of language in perception and action in the
> world. Although deep-learning has greatly enhanced methods for such
> grounded language understanding, it is difficult to ensure that the data
> used to train such models covers all of the concepts that a robot might
> encounter in practice. Therefore, we have developed methods that can
> continue to learn from dialog with users during ordinary use by acquiring
> additional targeted training data from the responses to intentionally
> designed clarification and active learning queries. These methods use
> reinforcement learning to automatically acquire dialog strategies that
> support both effective immediate task completion as well as learning that
> improves future performance. Using both experiments in simulation and with
> real robots, we have demonstrated that these methods exhibit life-long
> learning that improves long-term performance.
>
> Bio: Raymond J. Mooney is a Professor in the Department of Computer
> Science at the University of Texas at Austin. He received his Ph.D. in 1988
> from the University of Illinois at Urbana/Champaign. He is an author of
> over 180 published research papers, primarily in the areas of machine
> learning and natural language processing. He was the President of the
> International Machine Learning Society from 2008-2011, program co-chair for
> AAAI 2006, general chair for HLT-EMNLP 2005, and co-chair for ICML 1990. He
> is a Fellow of AAAI, ACM, and ACL and the recipient of the Classic Paper
> award from AAAI-19 and best paper awards from AAAI-96, KDD-04, ICML-05 and
> ACL-07.
>
> *Host:* *David McAllester* <mcallester at ttic.edu>
>
>
>
>
> [image: image.png]
>
>
>
>
> Mary C. Marre
> Faculty Administrative Support
> *Toyota Technological Institute*
> *6045 S. Kenwood Avenue*
> *Chicago, IL  60637*
> *mmarre at ttic.edu <mmarre at ttic.edu>*
>
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