Solving crossword puzzles as probabilistic constraint satisfaction
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
The Distributed Constraint Satisfaction Problem: Formalization and Algorithms
IEEE Transactions on Knowledge and Data Engineering
A Market Protocol for Decentralized Task Allocation
ICMAS '98 Proceedings of the 3rd International Conference on Multi Agent Systems
An asynchronous complete method for distributed constraint optimization
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Distributed constraint optimization for multiagent systems
Distributed constraint optimization for multiagent systems
Task allocation via coalition formation among autonomous agents
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Preprocessing techniques for accelerating the DCOP algorithm ADOPT
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
An intelligent approach to surgery scheduling
PRIMA'10 Proceedings of the 13th international conference on Principles and Practice of Multi-Agent Systems
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Researchers building multi-agent algorithms typically work with problems abstracted away from real applications. The abstracted problem instances allow systematic and detailed investigations of new algorithms. However, a key question is how to apply algorithm, developed on an abstract problem, in a real application. In this paper, we report on what was required to apply a particular distributed resource allocation algorithm developed for an abstract coordination problem in a real hardware application. A probabilistic representation of resources and tasks was used to deal with uncertainty and dynamics and local reasoning was used to deal with delays in the distributed resource allocation algorithm. The probabilistic representation and local reasoning enabled the use of the multi-agent algorithm which, in turn, improved the overall performance of the system.