Efficient methods for near-optimal sequential decision making under uncertainty

14 years 7 months ago

Download fias.uni-frankfurt.de

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.

Christos Dimitrakakis

Real-time Traffic

Bayesian Inference | Control | Optimal Stopping | Reinforcement Learning | Risk | Sequential Decision Making | Utility |

posted by olethros

Post Info
More Details (n/a)

Added	20 Apr 2010
Updated	20 Apr 2010
Type	Others
Year	2010
Where	Book chapter in Interactive Collaborative Information Systems, in the series Studies in Computational Intelligence
Authors	Christos Dimitrakakis

Comments (0)

	Complexity of Stochastic Branch and Bound Methods for Belief Tree Search in Bayesian Reinforcement Learning 509 views
	Reid et al.'s Distance Bounding Protocol and Mafia Fraud Attacks over Noisy Channels 545 views
	Rollout Sampling Approximate Policy Iteration 334 views
	Bayesian variable order Markov models. 404 views
	Statistical Decision Making for Authentication and Intrusion Detection 634 views

Sciweavers

Efficient methods for near-optimal sequential decision making under uncertainty

Bayesian Inference | Control | Optimal Stopping | Reinforcement Learning | Risk | Sequential Decision Making | Utility |

Explore & Download

Productivity Tools

Document Tools

Image Tools

Sciweavers