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Research team
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Overall Goal and TechniquesThe
CORE project is developing new approaches to speeding up reasoning and
search by synthesizing ideas from the study of phase transition phenomena
in problem distributions, decision-theoretic control of reasoning, Bayesian
inference, and machine learning. Current research centers on the
construction of probabilistic models that can predict the run time of problem
solving algorithms, and the development of solver control strategies that
take advantage of such predictive models to improve performance.
Work from the CORE project is showing how large, hard search and reasoning
problems can be solved in practice by leveraging the inherent uncertainty
and variability in runtime of combinatorial algorithms.
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PresentationsA Bayesian Approach to Tackling Hard Computational ProblemsEric Horvitz, presented at the the 17th Conference on Uncertainty in Artificial Intelligence (UAI-2001). Learning
to Search.
LinksInternal CORE Project page (password protected)Project SupportThis material is based upon work supported by the National Science Foundation under Grant No. 0120307. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. We also gratefully acknowledge support from the Microsoft Corporation, and the Intelligent Information Systems Institute.
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