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

This paper considers a novel approach to scalable mul-tiagent resource allocation in dynamic settings. We propose an approximate solution in which each resource consumer is represented by an independent MDP-based agent that models expected utility using an average model of its expected access to resources given only limited information about all other agents. A global auction-based mechanism is proposed for allocations based on expected regret. We assume truthful bidding and a cooperative coordination mechanism, as we are considering healthcare scenarios. We illustrate the per-formance of our coordinated MDP approach against a Monte-Carlo based planning algorithm intended for large-scale applications, as well as other approaches suitable for allocating medical resources. The evalua-tions show that the global utility value across all con-sumer agents is closer to optimal when using our algo-rithms under certain time constraints, with low compu-tational cost. As such, we offer a promising approach for addressing complex resource allocation problems that arise in healthcare settings