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Probabilistic Reasoning as Symbolic Evaluation

Principal Investigator:  Rodrigo de Salvo Braz

Mailing address:
AI Center
SRI International
333 Ravenswood Avenue
Menlo Park, CA 94025-3493

AIC Program:  Natural Language

Project home page at http://code.google.com/p/aic-praise/

   Project Description

PRAiSE is part of the Probabilistic Programming for Advanced Machine Learning (PPAML) DARPA program. PPAML's goal is to further the research towards frameworks in which the user can specify probabilistic generative models using a Turing-complete programming language, and query this model in various ways.

The PRAiSE project provides research in lifted inference for PPAML. Lifted inference is a symbolic way of performing inference directly at the higher-order level in which probabilistic programs are specified, as opposed to grounding them to propositional graphical models. The latter option is better understood today, but much slower. PRAiSE seeks to make probabilistic programs inference scalable.

The main advantages of PRAiSE will be the solving of expressive probabilistic models, specified in well-known, convenient programming languages, producing comprehensible output, explanations, and debugging information.

Typical areas of applications will be natural language processing, vision, planning, and common-sense reasoning.

   Personnel

Name Title E-mail
OReilly, Ciaran Senior Computer Scientist

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