Resource allocation among agents with preferences induced by factored MDPs

Distributing scarce resources among agents in a way that maximizes the social welfare of the group is a computationally hard problem when the value of a resource bundle is not linearly decomposable. Furthermore, the problem of determining the value of a resource bundle can be a significant computational challenge in itself, such as for an agent operating in a stochastic environment, where the value of a resource bundle is the expected payoff of the optimal policy realizable given these resources. Recent work has shown that the structure in agents' preferences induced by stochastic policy-optimization problems (modeled as MDPs) can be exploited to solve the resource-allocation and the policyoptimization problems simultaneously, leading to drastic (often exponential) improvements in computational efficiency. However, previous work used a flat MDP model that scales very poorly. In this work, we present and empirically evaluate a resource-allocation mechanism that achieves much bette...

• Type: Conference
• Year: 2006
• Where: ATAL
• Authors: Dmitri A. Dolgov, Edmund H. Durfee
• Keywords: ATAL 2006, Intelligent Agents, MDP Models, Resource, Resource Bundle