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- SLAC National Accelerator LaboratoryUS86 papers
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6 papers · 2 filters
Representing and Reasoning With Probabilistic Knowledge: A Bayesian Approach
Marie desJardins
PAGODA (Probabilistic Autonomous Goal-Directed Agent) is a model for autonomous learning in probabilistic domains [desJardins, 1992] that incorporates innovative techniques for usi…
Learning Bayesian Networks with Local Structure
Nir Friedman, Moises Goldszmidt
In this paper we examine a novel addition to the known methods for learning Bayesian networks from data that improves the quality of the learned networks. Our approach explicitly r…
Context-Specific Independence in Bayesian Networks
Craig Boutilier, Nir Friedman, Moises Goldszmidt +1
Bayesian networks provide a language for qualitatively representing the conditional independence properties of a distribution. This allows a natural and compact representation of t…
Constraining Influence Diagram Structure by Generative Planning: An Application to the Optimization of Oil Spill Response
John Mark Agosta
This paper works through the optimization of a real world planning problem, with a combination of a generative planning tool and an influence diagram solver. The problem is taken f…
Sequential Update of Bayesian Network Structure
Nir Friedman, Moises Goldszmidt
There is an obvious need for improving the performance and accuracy of a Bayesian network as new data is observed. Because of errors in model construction and changes in the dynami…
Continuous Value Function Approximation for Sequential Bidding Policies
Craig Boutilier, Moises Goldszmidt, Bikash Sabata
Market-based mechanisms such as auctions are being studied as an appropriate means for resource allocation in distributed and mulitagent decision problems. When agents value resour…