The complexity of Markov decision processes
Mathematics of Operations Research
T&Aelig;MS: a framework for environment centered analysis and design of coordination mechanisms
Foundations of distributed artificial intelligence
Coverage for robotics – A survey of recent results
Annals of Mathematics and Artificial Intelligence
The Advantages of Compromising in Coalition Formation with Incomplete Information
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 2
Multi-task overlapping coalition parallel formation algorithm
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Coalition game-based distributed coverage of unknown environments by robot swarms
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 3
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
BSA-CM: A Multi-robot Coverage Algorithm
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 02
Methods for task allocation via agent coalition formation
Artificial Intelligence
An Introduction to MultiAgent Systems
An Introduction to MultiAgent Systems
Distributed coordination of mobile agent teams: the advantage of planning ahead
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Non-additive multi-objective robot coalition formation
Expert Systems with Applications: An International Journal
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In this work, we focus on problems modeled as a set of tasks to be scheduled and accomplished by mobile autonomous devices that communicate via a mobile ad hoc network. In such situations, the communication cost, computational efforts and environment uncertainty are key challenges. It is intuitive to consider that keeping information about tasks globally known by devices can provide better schedules. However, there are some contexts - such as those where tasks require startup based on location - where information restricted to coalitions of devices can still produce satisfactory scheduling. The existing heuristics, however, do not consider this approach. In this paper, we propose a multiagent system that coordinates the dynamic formation of overlapping coalitions and the scheduling of tasks within them. Heuristics for calculating the size of coalitions, as well as for scheduling tasks are proposed based on a Markov decision process. The system is applied to solve the problem of area coverage in a simulated environment and the results show that good schedules are obtained with lower cost of communication and computation compared with the solution based on globally known information.