A Multiagent Task Associated MDP (MTAMDP) Approach to Resource Allocation

Pierrick Plamondon, Brahim Chaib-draa, Abder Rezak Benaskeur

This paper contributes to solving effectively a specific type of real-time stochastic resource allocation problem known to be NP-Complete. Its main distinction is the high number of possible interacting actions to execute in a group of tasks. To address this complex resource management problem, an adaptation of the Multiagent Markov Decision Process (MMDP) model which centralizes the computation of interacting resources is proposed. This adaptation is called Multiagent Task Associated Markov Decision Process (MTAMDP) and produces a near-optimal solution policy in a much lower time than a standard MMDP approach. In a MTAMDP, a planning agent computes a policy for each resource, and all these planning agents are coordinated by a central agent. MTAMDPs enable practically solving our NP-Complete problem.


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