Autonomous Agents and Multi-Agent Systems

, Volume 17, Issue 2, pp 190–250

Formal models and algorithms for decentralized decision making under uncertainty


DOI: 10.1007/s10458-007-9026-5

Cite this article as:
Seuken, S. & Zilberstein, S. Auton Agent Multi-Agent Syst (2008) 17: 190. doi:10.1007/s10458-007-9026-5


Over the last 5 years, the AI community has shown considerable interest in decentralized control of multiple decision makers or “agents” under uncertainty. This problem arises in many application domains, such as multi-robot coordination, manufacturing, information gathering, and load balancing. Such problems must be treated as decentralized decision problems because each agent may have different partial information about the other agents and about the state of the world. It has been shown that these problems are significantly harder than their centralized counterparts, requiring new formal models and algorithms to be developed. Rapid progress in recent years has produced a number of different frameworks, complexity results, and planning algorithms. The objectives of this paper are to provide a comprehensive overview of these results, to compare and contrast the existing frameworks, and to provide a deeper understanding of their relationships with one another, their strengths, and their weaknesses. While we focus on cooperative systems, we do point out important connections with game-theoretic approaches. We analyze five different formal frameworks, three different optimal algorithms, as well as a series of approximation techniques. The paper provides interesting insights into the structure of decentralized problems, the expressiveness of the various models, and the relative advantages and limitations of the different solution techniques. A better understanding of these issues will facilitate further progress in the field and help resolve several open problems that we identify.


Decentralized decision making Cooperative agents Multi-agent planning 

Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  1. 1.School of Engineering and Applied SciencesHarvard UniversityCambridgeUSA
  2. 2.Department of Computer ScienceUniversity of MassachusettsAmherstUSA

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