Learning, Logic and Probability: A Unified View

Speaker:	Professor Pedro Domingos
		Department of Computer Science and
		Engineering, University of Washington at Seattle, USA.

Title: 		Learning, Logic and Probability: A Unified View

Date:		Thursday, 3 August, 2006

Time:		10:30am -11:30am

Venue:		Room 5564 (via lift nos.27/28)
		HKUST

Abstract:

AI systems must be able to learn, reason logically, and handle
uncertainty. While much research has focused on each of these goals
individually, only recently have we begun to attempt to achieve all three
at once. In this talk I will describe Markov logic, a representation that
combines first-order logic and probabilistic graphical models, and
algorithms for learning and inference in it. Syntactically, Markov logic
is first-order logic augmented with a weight for each formula.
Semantically, a set of Markov logic formulas represents a probability
distribution over possible worlds, in the form of a Markov network with
one feature per grounding of a formula in the set, with the corresponding
weight. Formulas are learned from relational databases using inductive
logic programming techniques. Weights can be learned either generatively
(using pseudo-likelihood optimization) or discriminatively (using a voted
perceptron algorithm). Inference is performed by a weighted satisfiability
solver and/or a Gibbs sampler, operating on the minimal subset of the
ground network required for answering the query. Experiments in link
prediction, entity resolution and other problems illustrate the promise of
this approach.

(Joint work with Stanley Kok, Matt Richardson and Parag Singla.)

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Biography:

Pedro Domingos is Associate Professor of Computer Science and Engineering
at the University of Washington. His research interests are in artificial
intelligence, machine learning and data mining. He received a PhD in
Information and Computer Science from the University of California at
Irvine, and is the author or co-author of over 100 technical publications.
He is a member of the advisory board of JAIR, a member of the editorial
board of the Machine Learning journal, and a co-founder of the
International Machine Learning Society. He was program co-chair of
KDD-2003, and has served on numerous program committees. He 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 and PKDD-05.