An Efficient Resource Allocation Approach in Real-Time Stochastic Environment
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.
KeywordsResource Allocation Multiagent System Markov Decision Process Resource Type Resource Allocation Problem
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- 1.Aberdeen, D., Thiebaux, S., Zhang, L.: Decision-theoretic military operations planning. In: Proceedings of the International Conference on Automated Planning and Scheduling, Whistler, Canada, June 3–7 (2004)Google Scholar
- 2.Bertsekas, D.: Rollout algorithms for constrained dynamic programming. Technical report 2646, Lab. for Information and Decision Systems, MIT, Mass., USA (2005)Google Scholar
- 3.Boutilier, C.: Sequential optimality and coordination in multiagent systems. In: Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence (IJCAI-1999), Stockholm, August 1999, pp. 478–485 (1999)Google Scholar
- 5.Nilsson, N.J.: Principles or Artificial Intelligence. Tioga Publishing, Palo Alto, Ca (1980)Google Scholar
- 6.Plamondon, P., Chaib-draa, B., Benaskeur, A.: Decomposition techniques for a loosely-coupled resource allocation problem. In: Proceedings of the IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT 2005) (September 2005)Google Scholar
- 7.Plamondon, P., Chaib-draa, B., Benaskeur, A.: A multiagent task associated mdp (mtamdp) approach to resource allocation. In: AAAI 2006 Spring Symposium on Distributed Plan and Schedule Management (March 2006)Google Scholar
- 8.Russell, S.J., Zimdars, A.: Q-decomposition for reinforcement learning agents. In: ICML, pp. 656–663 (2003)Google Scholar
- 10.Wu, C.C., Castanon, D.A.: Decomposition techniques for temporal resource allocation. Technical report: Afrl-va-wp-tp-2004-311, Air Force Research Laboratory, Air force base, OH (2004)Google Scholar
- 11.Wu, J., Durfee, E.H.: Automated resource-driven mission phasing techniques for constrained agents. In: Proceedings of the Fourth International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2005), August 2005, pp. 331–338 (2005)Google Scholar
- 12.Zhang, W.: Modeling and solving a resource allocation problem with soft constraint techniques. Technical report: Wucs-2002-13, Washington University, Saint-Louis, Missouri (2002)Google Scholar