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AIC Seminar Series

Probabilistic Inference Modulo Theories

Rodrigo de Salvo BrazArtificial Intelligence Center, SRI International[Home Page]

Date:  Thursday, September 8th 2016 at 4:00pm

Location:  EK255 (SRI E building)  (Directions)

Webex: 

WebEx recording (SRI WebEx access required):

https://sri-meetings.webex.com/sri-meetings/ldr.php?RCID=aa6db41907d30a75d56f7b35d4d06cd4

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https://wiki.sri.com/download/attachments/4919820/Slides.pptx?version=1&modificationDate=1489787194294&api=v2

Audio: 

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   Abstract

Probabilistic inference algorithms are usually defined in terms of very simple representations, such as tables or decision diagrams over discrete variables. This makes it much harder to develop applications which would be more directly encoded by logic representations and operators (such as integers, reals, and algebraic data types). In this talk, I present a framework in which probabilistic inference is performed over logic theories, much in the same way Satisfiability Modulo Theories expands SAT solvers from propositional logic to richer theories. Currently, we operate on equality over categorical types, difference arithmetic over bounded integers, and linear real arithmetic theories. A demo of our system, AIC-PRAiSE, is available at http://aic-sri-international.github.io/aic-praise/.

   Bio for Rodrigo de Salvo Braz

Rodrigo de Salvo Braz is a Senior Computer Scientist at the Artificial Intelligence Center at SRI. He is the PI of the SRI team in the Probabilistic Programming for Advanced Machine Learning (PPAML) DARPA program. He has worked on lifted first-order probabilistic inference during his PhD at the University of Illinois at Urbana Champaign, and on the BLOG (Bayesian Logic) probabilistic programming language during a postdoc at UC Berkeley.

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