Adaptive control of constrained Markov chains: criteria and policies
Annals of Operations Research
Computationally Manageable Combinational Auctions
Management Science
Bidding and allocation in combinatorial auctions
Proceedings of the 2nd ACM conference on Electronic commerce
An Algorithm for Optimal Winner Determination in Combinatorial Auctions
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Solving concisely expressed combinatorial auction problems
Eighteenth national conference on Artificial intelligence
Dynamic Programming
Mechanism design and deliberative agents
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Computationally-efficient combinatorial auctions for resource allocation in weakly-coupled MDPs
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Resource allocation among agents with preferences induced by factored MDPs
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Integrated resource allocation and planning in stochastic multiagent environments
Integrated resource allocation and planning in stochastic multiagent environments
Bidding languages for combinatorial auctions
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Lagrangian Relaxation for Large-Scale Multi-agent Planning
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 02
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Optimal resource scheduling in multiagent systems is a computationally challenging task, particularly when the values of resources are not additive. We consider the combinatorial problem of scheduling the usage of multiple resources among agents that operate in stochastic environments, modeled as Markov decision processes (MDPs). In recent years, efficient resource-allocation algorithms have been developed for agents with resource values induced by MDPs. However, this prior work has focused on static resource-allocation problems where resources are distributed once and then utilized in infinite-horizon MDPs. We extend those existing models to the problem of combinatorial resource scheduling, where agents persist only for finite periods between their (predefined) arrival and departure times, requiring resources only for those time periods. We provide a computationally efficient procedure for computing globally optimal resource assignments to agents over time. We illustrate and empirically analyze the method in the context of a stochastic job-scheduling domain.