590AI Autumn '01

Artificial Intelligence Hit Parade

Greetings, and welcome to 590AI, Autumn 2001!  The focus of this seminar is slightly different this year - we'll be reading the Top Ten recent papers in AI, as determined by a broad-based and statistically-sophisticated survey covering the AI faculty in this department.   If you're interested in AI, be here or be...not well-rounded.

Administrivia

Like most all other classes in the department, CSE 590AI has a mailing list, managed by majordomo.  Please sign up by sending a message to majordomo@cs.washington.edu with the text subscribe cse590ai in the body.  You can view an archive of the mailing list online as well.

Meeting time: Wednesday 4:30 - 5:20
Meeting place: EE1 045
Organizer: Oren Etzioni <etzioni@cs.washington.edu>

About credits

590AI is offered as a 1 - 3 credit course.  Signing up for one credit is, of course, the minimum, and the minimum will be expected of you: attend the seminars, listen to the presentation, and participate in discussions.  If you'd just like to get a feel for AI, then sign up for one credit of 590AI and enjoy the presentations.

If, on the other hand, you have an active interest in AI and would like to present during the quarter, then you should sign up for 3 credits of 590AI.  The list of Hit Parade papers is below,  Don't worry, we're not asking for AAAI conference talks, here - we're more casual than that.  What we're after is a talk that is accessible to folks who have read (or at least skimmed) the paper you're presenting, but who may not have read any other related work.  Please feel free to form clusters and jointly present.

About cookies

Speaking of well-rounded:  Each week's seminar will be catered by one of the students in the class.  The "Cookie supplier" should bring enough treats (typically cookies, but feel free to be creative) for all.  Fare from the past has ranged from Peppridge Farms cookies bought at the HUB newstand 5 minutes before class time, all the way to peanut brittle, brownies, and Kool-aid, all prepared by hand.  If you're worried about being adequate, just don't follow Steve Wolfman.

The schedule

Wednesday Paper Presenter(s) Discussion leader Cookie supplier
10/3        
10/10 Genetic Algorithms and Artificial Life  Alex Yates, Adam Carlson   Oren Etzioni
10/17 Cooperative Cognition Henry Kautz    Pat Tressel
10/24 The Semantic Web Mausam Oren Etzioni Julie Goldberg
10/31 no meeting      
11/7 The great Semantic Web debate and food fight* various* Jayant Madhavan Alex Yates
11/14 Authoritative Sources in a Hyperlinked Environment Deepak Verma Dave Hsu Ana-Maria Popescu
11/21 no meeting      
11/28 Integrated Natural Spoken Dialogue System of Jijo-2 Mobile Robot for Office Services Julie Goldberg Sarah Schwarm Don Patterson
A Unified Framework of Map Learning with a Hierarchy of Probabilistic Maps Nan Li Cody Kwok
12/5 Automated Construction of Database Interfaces: Integrating Statistical and Relational Learning for Semantic Parsing Kevin Sikorski Ana-Maria Popescu Sarah Schwarm
12/12 Task Decomposition, Dynamic Role Assignment, and Low-Bandwidth Communication for Real-Time Strategic Teamwork Ana-Maria Popescu, Dave Grimes Don Patterson Nan Li

Debate and food fight how-to

List of papers

Hideki Asoh and Toshihiro Matsui, A Unified Framework of Map Learning with a Hierarchy of Probabilistic Maps

Tim Berners-Lee, James Hendler, and Ora Lassila, The Semantic Web

Henry Kautz, Cooperative Cognition

(Please see link sent to the course mailing list.)
Joel Kleinberg, Authoritative Sources in a Hyperlinked Environment

Toshihiro Matsui, Hideki Asoh, John Fry, Youichi Motomura, Futoshi Asano ,Takio Kurita, Isao Hara, and Nobuyuki Otsu, Integrated Natural Spoken Dialogue System of Jijo-2 Mobile Robot for Office Services

Melanie Mitchell and Stephanie Forrest, Genetic Algorithms and Artificial Life

Abstract:  Genetic algorithms are computational models of evolution that play a central role in many artificial-life models.  We review the history and current scope of research on genetic algorithms in artificial life, using illustrative examples in which the genetic algorithm is used to study how learning and evolution interact, and to model ecosystems, immune system, cognitive systems, and social systems.  We also outline a number of open questions and future directions for genetic algorithms in artificial-life research.
Peter Stone and Manuela Veloso, Task Decomposition, Dynamic Role Assignment, and Low-Bandwidth Communication for Real-Time Strategic Teamwork

Lapoon R. Tang and Raymond J. Mooney, Automated Construction of Database Interfaces: Integrating Statistical and Relational Learning for Semantic Parsing


Send corrections to
tressel@cs.washington.edu