Combinatorial optimization
Solving very large weakly coupled Markov decision processes
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
On the Use of Integer Programming Models in AI Planning
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Integer optimization models of AI planning problems
The Knowledge Engineering Review
Automated resource-driven mission phasing techniques for constrained 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
Mixed-integer linear programming for transition-independent decentralized MDPs
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Resource allocation among agents with MDP-induced preferences
Journal of Artificial Intelligence Research
Planning for Coordination and Coordination for Planning
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Resource-driven mission-phasing techniques for constrained agents in stochastic environments
Journal of Artificial Intelligence Research
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Exchanging scarce resources during execution among a group of agents is one way to improve the overall performance in multiagent systems with limited shared resources, but implementing optimal sequential resource allocation is often a nontrivial problem in complex systems with uncertainties. In this paper, we present an MILP-based algorithm that can automatically break a large mission into multiple phases and make optimal resource (re)allocations at the entry of each phase. We illustrate our algorithms through several increasingly complex classes of sequential resource allocation problems, and show through experiments that our techniques can increase agents' rewards for varying levels of constraints on resources and constraints on exchanging resources.