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
9783642116872 (ISBN)
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