UTCS Colloquia/AI - Pedro Domingos/University of Washington, "Unifying Logic and Probability: A Progress Report", ACES 2.402

Contact Name: 
Jenna Whitney
Date: 
May 3, 2011 11:00am - 12:00pm

There is a sign-up schedule for this event that can be found at

http://www.cs.utexas.edu/department/webevent/utcs/events/cgi/list_event

s.cgi

Type of Talk: UTCS Colloquia/AI

Speaker/Affiliation: Pedro

Domingos/University of Washington

Talk Audience: UTCS Faculty, Grad S

tudents, Undergrads, and Outside Interested Parties

Date/Time: Tuesd

ay, May 3, 2011, 11:00 a.m.

Location: ACES 2.402

Host: Raymond
J. Mooney

Talk Title: Unifying Logic and Probability: A Progress Repo

rt

Talk Abstract:
Intelligent agents must be able to handle the comp

lexity and uncertainty of the real world. First-order logic is a good repre

sentation for complexity, and probabilistic graphical models for uncertain

ty. Unifying the two is a long-standing goal of AI. This talk samples the s

tate of the art in this area, in four parts: representation, inference,

learning, and applications. First, I will introduce Markov logic, a lang

uage that combines logic and probability by attaching weights to first-orde

r formulas and viewing them as templates for features of Markov random fiel

ds. Second, I will describe probabilistic theorem proving, an inference p

rocedure that combines theorem proving and graphical model inference. Third

, we look at statistical relational learning, with a focus on learning th

e structure and weights of Markov logic networks. Fourth, we apply these t

echniques to problems in natural language processing, including coreferenc

e resolution and semantic parsing. (Joint work with Jesse Davis, Vibhav Go

gate, Stanley Kok, Daniel Lowd, Aniruddh Nath, Hoifung Poon, Math Rich

ardson, Parag Singla, Marc Sumner, and Jue Wang.)

Speaker Bio:

nPedro Domingos is Associate Professor of Computer Science and Engineering

at the University of Washington. His research interests are in artificial i

ntelligence, machine learning and data mining. He received a PhD in Inform

ation and Computer Science from the University of California at Irvine, an

d is the author or co-author of over 150 technical publications. He is a me

mber of the editorial board of the Machine Learning journal, co-founder of
the International Machine Learning Society, and past associate editor of

JAIR. He was program co-chair of KDD-2003 and SRL-2009, and has served on

numerous program committees. He is a AAAI Fellow, and has received several
awards, including a Sloan Fellowship, an NSF CAREER Award, a Fulbright

Scholarship, an IBM Faculty Award, and best paper awards at KDD-98, KDD-

99, PKDD-05 and EMNLP-09.