Efficient methods for near-optimal sequential decision making under uncertainty
Book chapter, 2010

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.

Author

Christos Dimitrakakis

Chalmers, Computer Science and Engineering (Chalmers), Computing Science (Chalmers)

Interactive Collaborative Information Systems, Studies Computational Intelligence Volume 281

Areas of Advance

Information and Communication Technology

Subject Categories

Computer and Information Science

Probability Theory and Statistics

DOI

10.1007/978-3-642-11688-9_5

ISBN

9783642116872

More information

Latest update

12/13/2018