Forum for Artificial Intelligence

[ About FAI   |   Upcoming talks   |   Past talks ]

About FAI

The Forum for Artificial Intelligence meets every other week (or so) to discuss scientific, philosophical, and cultural issues in artificial intelligence. Both technical research topics and broader inter-disciplinary aspects of AI are covered, and all are welcome to attend!

If you would like to be added to the FAI mailing list, or have any questions or comments, please send email to Manish Saggar or Joseph Reisinger .

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Upcoming talks

Thursday, September 11
3pm, TAY 3.128
Chad Jenkins
Brown University
Learning in Human-Robot Teams
Friday, September 12
11:00am, TAY 3.128
Jesse Davis
University of Washington
Statistical Relational Learning, Diagnosing Breast Cancer and Transfer Learning

Thursday, September 11, 3:00pm

Coffee at 2:45pm

TAY 3.128

Learning in Human-Robot Teams

Chad Jenkins   [homepage]

Brown University

A principal goal of robotics is to realize embodied systems that are effective collaborators for human endeavors in the physical world. Human-robot collaborations can occur in a variety of forms, including autonomous robotic assistants, mixed-initiative robot explorers, and augmentations of the human body. For these collaborations to be effective, human users must have the ability to realize their intended behavior into actual robot control policies. At run-time, robots should be able to manipulate an environment and engage in two-way communication in a manner suitable to their human users. Further, the tools for programming, communicating with, and manipulating using robots should be accessible to the diverse sets of technical abilities present in society.
Towards the goal of effective human-robot collaboration, our research has pursued the use of learning and data-driven approaches to robot programming, communication, and manipulation. Learning from demonstration (LfD) has emerged as a central theme of our efforts towards natural instruct of autonomous robots by human users. In robot LfD, the desired robot control policy is implicit in human demonstration rather than explicitly coded in a computer program.
In this talk, I will describe our LfD-based work in policy learning using Gaussian Process Regression and humanoid imitation learning through spatio-temporal dimension reduction. This work is supported by our efforts in markerless, inertial-based, and physics-based human kinematic tracking, notably our indoor-outdoor person following system developed in collaboration with iRobot Research. I will additionally argue that collaboration in human-robot teams can be modeled by Markov Random Fields (MRFs), allowing for unification of existing multi-robot algorithms, application of belief propagation, and faithful modeling of experimental findings from cognitive science. Time permitting, I will also discuss our work learning tactile and force signatures to distinguish successful versus unsuccessful grasping on the NASA Robonaut.

About the speaker:

Odest Chadwicke Jenkins, Ph.D., is an Assistant Professor of Computer Science at Brown University. Prof. Jenkins earned his B.S. in Computer Science and Mathematics at Alma College (1996), M.S. in Computer Science at Georgia Tech (1998), and Ph.D. in Computer Science at the University of Southern California (2003). In 2007, he received Young Investigator funding from the Office of Naval Research and the Presidential Early Career Award for Scientists and Engineers (PECASE) for his work in learning primitive models of human motion for humanoid robot control and kinematic tracking.

Friday, September 12, 11:00am

Coffee at 10:45am

TAY 3.128

Statistical Relational Learning, Diagnosing Breast Cancer and Transfer Learning

Dr. Jesse Davis   [homepage]

University of Washington

Standard inductive learning makes two key assumptions about the structure of the data. First, it requires that all examples are independent and identically distributed (iid). Second, it requires that the training and test instances come from the same distribution. Decades of research have produced many sophisticated techniques for solving this task. Unfortunately, in real applications these assumptions are often violated. In the first part of this talk, I will motivate the need to handle non-iid data through the concrete task of predicting whether an abnormality on a mammogram is malignant. I will describe the SAYU algorithm, which automatically constructs relational features. Our system makes significantly more accurate predictions than both radiologists and other machine learning techniques on this task. Furthermore, we identified a novel feature that is indicative of malignancy. In the second part of this talk, I will discuss a transfer learning algorithm that removes both restrictions made by standard inductive learners. In shallow transfer, test instances are from the same domain, but have a different distribution. In deep transfer, test instances are from a different domain entirely (i.e., described by different predicates). Humans routinely perform deep transfer, but few learning systems are capable of it. I will describe an approach based on a form of second-order Markov logic, which discovers structural regularities in the source domain in the form of Markov logic formulas with predicate variables, and instantiates these formulas with predicates from the target domain. Using this approach, we have successfully transferred learned knowledge between a molecular biology domain and a Web one. The discovered patterns include broadly useful properties of predicates, like symmetry and transitivity, and relations among predicates, like various forms of homophily.

About the speaker:

Jesse Davis is a post-doctoral researcher at the University of Washington. He received his Ph.D in computer science at the University of Wisconsin - Madison in 2007 and a B.A. in computer science from Williams College in 2002. His research interests include statistical relational learning, transfer learning, inductive logic programming and data mining for biomedical domains.

Past talks

Monday, August 25
3:00pm, PAR 301
Alexander Koller
Saarland University
Generation as planning
Friday, August 29
11:00am, ACES 2.402
Sameer S. Pradhan
BBN Technologies
OntoNotes: A Unified Relational Semantic Representation

Monday, August 25, 3:00pm

PAR 301

Generation as planning

Alexander Koller   [homepage]

Saarland University

Joint work with Matthew Stone and Ron Petrick.
The problem of natural language generation is intimately related to AI planning on many levels. In both problems, the computer has to search for a sequence of actions that combine in appropriate ways to achieve a given goal; in the case of generation, these actions may correspond to uttering speech acts, sentences, or individual words. This has been recognized in the literature for several decades, but there is currently a revival of interest in exploring this connection, which has been sparked especially by the recent efficiency improvements in planning. In my talk, I will first show how sentence generation can be translated into a planning problem. This has the advantage that the (somewhat artificial) separation of sentence generation into microplanning and surface realization can be overcome. Furthermore, each plan action captures the complete grammatical, semantic, and pragmatic preconditions and effects of uttering a single word. I will then present a new shared task for the generation community, in which the system must generate instructions in a virtual environment. I will discuss some problems that arise in this application -- particularly regarding the use of extralinguistic context in generation -- and propose some ideas on how they can be tackled using a planning approach.

About the speaker:


Friday, August 29, 11:00am

Coffee at 10:45am

ACES 2.402

OntoNotes: A Unified Relational Semantic Representation

Sameer S. Pradhan   [homepage]

BBN Technologies

[Sign-up schedule for individual meetings]

The OntoNotes project is creating a corpus of large-scale, accurate, and integrated annotation of multiple levels of the shallow semantic structure in text. Such rich, integrated annotation covering many levels will allow for richer, cross-level models enabling significantly better automatic semantic analysis. At the same time, it demands a robust, efficient, scalable mechanism for storing and accessing these complex inter-dependent annotations. We describe a relational database representation that captures both the inter- and intra-layer dependencies and provide details of an object-oriented API for efficient, multi-tiered access to this data.

The OntoNotes project is funded by DARPA under the GALE program and is a collaborative effort between BBN Technologies, University of Colorado, University of Pennsylvania, and Information Sciences Institute at the University of Southern California.

About the speaker:

Sameer Pradhan completed his PhD in Computer Science at the University of Colorado, Boulder in 2005 under the guidance of Profs. Wayne Ward, James H. Martin, and Daniel Jurafsky. He is currently working as a Scientist at BBN Technologies . His prior academic pursuits include a Master of Science in Industrial Engineering from Alfred University, NY and a Bachelor's degree in Production Engineering from Victoria Jubilee Technical Institute, Bombay.

Past Schedules

Fall 2007 - Spring 2008

Fall 2006 - Spring 2007

Fall 2005 - Spring 2006

Spring 2005

Fall 2004

Spring 2004

Fall 2003

Spring 2003

Fall 2002

Spring 2002

Fall 2001

Spring 2001

Fall 2000

Spring 2000