## First Order Probabilistic Logic

Eyal Amir^{1}, Bhaskara Marthi, Brian Milch, Hanna Pasula, and Eric Xing

(Professor Stuart J. Russell)

(DOD-ONR) MURI FD N00014-01-1-0890, (DOD-ONR) MURI FD N00014-00-1-0637, and (NSF) ECS-9873474

First-order probabilistic languages (FOPLs) combine the expressive power of
first-order logic with the uncertainty handling of probability theory. Our
group aims to apply these languages to interesting real-world domains.
However, as we consider FOPLs of increasing complexity, inference grows
difficult; and so, when we define useful languages, we must simultaneously
work on developing tractable inference algorithms.

In recent years, several families of FOPLs have been proposed, but no
clear consensus has emerged about what the "right" language is, either
in general, or in specific application domains. We are investigating
these proposed languages with respect to expressive power, efficiency of
inference, and ease of modeling.

Much of our current research is inspired by Avi Pfeffer's probabilistic
relational models (PRMs), a FOPL family based on semantic networks. We have
extended PRMs in several ways, most notably by removing the unique-names
assumption, and thus introducing uncertainty over the number of objects
present in the system. This has permitted us to apply our work to problems
such as data association (vehicle tracking), and, more recently, citation
clustering. We plan to continue working on information extraction from
the web, and also on problems from computational biology, such as
modeling motifs in DNA sequences.

Exact inference in these domains is, in general, not tractable, and so we
have developed an approximate approach based on the Markov Chain Monte Carlo
algorithm, augmented by intelligent proposal distributions. We are
also developing deterministic methods, in which the relational
structure of the domain is used to motivate a structured variational
approximation to the true posterior.

^{1}Postdoctoral Researcher

Send mail to the author : (bhaskara@eecs.berkeley.edu)

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