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This paper considers a novel approach to scalable multiagent resourceallocation in dynamic settings. We propose an approximate solution in whicheach resource consumer is represented by an independent MDP-based agent thatmodels expected utility using an average model of its expected access toresources given only limited information about all other agents. A globalauction-based mechanism is proposed for allocations based on expected regret.We assume truthful bidding and a cooperative coordination mechanism, as we areconsidering healthcare scenarios. We illustrate the performance of ourcoordinated MDP approach against a Monte-Carlo based planning algorithmintended for large-scale applications, as well as other approaches suitable forallocating medical resources. The evaluations show that the global utilityvalue across all consumer agents is closer to optimal when using our algorithmsunder certain time constraints, with low computational cost. As such, we offera promising approach for addressing complex resource allocation problems thatarise in healthcare settings.

A Coordinated MDP Approach to Multi-Agent Planning for Resource Allocation, with Applications to Healthcare