Efficient methods for near-optimal sequential decision making under uncertainty

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.


Published in:
Studies in Computational Intelligence, 281, null, 125-153
Year:
2010
ISSN:
1860949X
Laboratories:




 Record created 2014-03-20, last modified 2018-09-13

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