Principles of artificial intelligence
Principles of artificial intelligence
Automated resource-driven mission phasing techniques for constrained agents
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Decomposition Techniques for a Loosely-Coupled Resource Allocation Problem
IAT '05 Proceedings of the IEEE/WIC/ACM International Conference on Intelligent Agent Technology
Sequential optimality and coordination in multiagent systems
IJCAI'99 Proceedings of the 16th international joint conference on Artifical intelligence - Volume 1
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We are interested in contributing to solving effectively a particular type of real-time stochastic resource allocation problem. Firstly, one distinction is that certain tasks may create other tasks. Then, positive and negative interactions among the resources are considered, in achieving the tasks, in order to obtain and maintain an efficient coordination. A standard Multiagent Markov Decision Process (MMDP) approach is too prohibitive to solve this type of problem in real-time. To address this complex resource management problem, the merging of an approach which considers the complexity associated to a high number of different resource types (i.e. Multiagent Task Associated Markov Decision Processes (MTAMDP)), with an approach which considers the complexity associated to the creation of task by other tasks (i.e. Acyclic Decomposition) is proposed. The combination of these two approaches produces a near-optimal solution in much less time than a standard MMDP approach.