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AIC Seminar Series

Reasoning about uncertainty and correlated beliefs

Hui WanSUNY at Stony Brook[Home Page]

Notice:  Hosted by Vinay Chaudhri. Note the non-usual day (Wednesday) and venue.

Date:  2010-04-28 at 16:00

Location:  EK255 (SRI E building); WebEx 1-888-355-1249, 749045 (sound), https://sri.webexone.com/webservice/wxr.aspx?_command=join&MK=486937772 (slides via Web)  (Directions)

   Abstract

Real-world applications often must deal with various kinds of uncertainty. For knowledge representation and reasoning in the presence of uncertainty, it is important to be able to integrate information from different sources and assess the overall quality of the combined evidence. Combining evidence from different sources is particularly hard when different pieces of evidence supplied by these sources are correlated and possibly are in conflict.

To address this challenge, I developed Belief Logic Programming (BLP), a novel reasoning framework that deals with uncertain and inconsistent information and is able to combine and correlate evidence obtained from non-independent information sources. BLP has well-defined declarative and fixpoint semantics, and an optimized query evaluation algorithm. BLP has strong relationships to defeasible reasoning, paraconsistent reasoning, and Dempster-Shafer theory of evidence.

At the end of my talk, I will briefly describe my contributions to the multi-institution project SILK, which is part of Project Halo, and the FLORA2 system, a powerful frame-based knowledge representation and reasoning system.

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