Sequential Decision Making for Intelligent Agents

Papers from the AAAI Fall Symposium

Book (2015)

Contributors

M.T.J. Spaan Algorithmics - EEMCS (editor)
F.A. Oliehoek Interactive Intelligence - EEMCS (editor)
Pascal Poupart (editor)
Research Group
Algorithmics (EEMCS) (TU Delft)
More Info
expand_more

Published Date

2015

Language

English

Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Software Technology

Research Group

Algorithmics

Abstract

Sequential decision making under uncertainty has gained significant traction in Artificial Intelligence. In many applications, dealing explicitly with uncertainty regarding the effects of actions, state of the environment and possibly the behavior of other agents is crucial to achieve satisfactory task performance. Decision-theoretic planning models like the Markov decision process (MDP), the partially observable MDP (POMDP) and their many multiagent extensions have emerged as the dominant paradigm for this purpose. This symposium provided a dedicated forum for researchers of computational sequential decision making under uncertainty to meet and share ideas.