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Efficient methods for near-optimal sequential decision making under uncertainty

Christos Dimitrakakis (Institutionen för data- och informationsteknik, Datavetenskap, Algoritmer (Chalmers))
Interactive Collaborative Information Systems, Studies in Computational Intelligence Volume 281 (2010)
[Kapitel]

This chapter discusses decision making under uncertainty. More specifically, it offers an overview of efficient Bayesian and distribution-free algorithms for making near-optimal sequential decisions under uncertainty about the environment. Due to the uncertainty, such algorithms must not only learn from their interaction with the environment but also perform as well as possible while learning is taking place. © 2010 Springer-Verlag Berlin Heidelberg.



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Denna post skapades 2013-12-17. Senast ändrad 2015-01-08.
CPL Pubid: 189649

 

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