Publication Type
Conference Proceeding Article
Version
submittedVersion
Publication Date
7-2004
Abstract
Distributed Constraint Optimization (DCOP) is an elegant formalism relevant to many areas in multiagent systems, yet complete algorithms have not been pursued for real world applications due to perceived complexity. To capably capture a rich class of complex problem domains, we introduce the Distributed Multi-Event Scheduling (DiMES) framework and design congruent DCOP formulations with binary constraints which are proven to yield the optimal solution. To approach real-world efficiency requirements, we obtain immense speedups by improving communication structure and precomputing best case bounds. Heuristics for generating better communication structures and calculating bound in a distributed manner are provided and tested on systematically developed domains for meeting scheduling and sensor networks, exemplifying the viability of complete algorithms.
Discipline
Artificial Intelligence and Robotics | Business | Operations Research, Systems Engineering and Industrial Engineering
Publication
AAMAS '04: Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems: New York, USA, July 19-23, 2004
First Page
310
Last Page
317
ISBN
9781581138641
Identifier
10.1109/AAMAS.2004.257
Publisher
IEEE Computer Society
City or Country
Los Alamitos, CA
Citation
MAHESWARAN, Rajiv; Tambe, Milind; Bowring, Emma; Pearce, Jonathan; and VARAKANTHAM, Pradeep.
Taking DCOP to the Real World: Efficient Complete Solutions for Distributed Event Scheduling. (2004). AAMAS '04: Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems: New York, USA, July 19-23, 2004. 310-317.
Available at: https://ink.library.smu.edu.sg/sis_research/935
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
http://portal.acm.org/citation.cfm?id=1018762
Included in
Artificial Intelligence and Robotics Commons, Business Commons, Operations Research, Systems Engineering and Industrial Engineering Commons