Learning, Logic, and Probability: A Unified View
(invited lecture for DS 2004)
Author: Pedro Domingos
Affiliation: Department of Computer Science & Engineering,
University of Washington, Seattle, USA
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 the full power of firstorder logic and
probabilistic graphical models, and algorithms for learning and
inference in it. Syntactically, Markov logic is firstorder 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 and weights are learned from relational databases using
inductive logic programming and iterative optimization of a
pseudolikelihood measure. Inference is performed by Markov chain
Monte Carlo over the minimal subset of the ground network required for
answering the query. Experiments in a realworld university domain
illustrate the promise of this approach.
(Joint work with Matt Richardson.)
©Copyright 2004 Author
