Sequential Decision Making for Intelligent Agents

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.