World modeling for the dynamic construction of real-time control plans
Artificial Intelligence
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
Multi-time models for temporally abstract planning
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
CPlan: a constraint programming approach to planning
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Between MDPs and semi-MDPs: a framework for temporal abstraction in reinforcement learning
Artificial Intelligence
Constrained Discounted Markov Decision Processes and Hamiltonian Cycles
Mathematics of Operations Research
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Crystalline Robots: Self-Reconfiguration with Compressible Unit Modules
Autonomous Robots
Planning and Resource Allocation for Hard Real-time, Fault-Tolerant Plan Execution
Autonomous Agents and Multi-Agent Systems
A Study of Index Structures for Main Memory Database Management Systems
VLDB '86 Proceedings of the 12th International Conference on Very Large Data Bases
A Mechanism for Managing the Buffer Pool in a Relational Database System Using the Hot Set Model
VLDB '82 Proceedings of the 8th International Conference on Very Large Data Bases
The Complexity of Decentralized Control of Markov Decision Processes
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Optimal and Hierarchical Controls in Dynamic Stochastic Manufacturing Systems: A Survey
Manufacturing & Service Operations Management
Combinatorial Auctions: A Survey
INFORMS Journal on Computing
Management Science
Evolution of the GPGP/TÆMS Domain-Independent Coordination Framework
Autonomous Agents and Multi-Agent Systems
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
Modeling Uncertainty and its Implications to Sophisticated Control in Tæms Agents
Autonomous Agents and Multi-Agent 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
Integrated resource allocation and planning in stochastic multiagent environments
Integrated resource allocation and planning in stochastic multiagent environments
Solving large TÆMS problems efficiently by selective exploration and decomposition
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Sequential resource allocation in multiagent systems with uncertainties
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Mission-phasing techniques for constrained agents in stochastic environments
Mission-phasing techniques for constrained agents in stochastic environments
An application view of COORDINATORS coordination managers for first responders
IAAI'04 Proceedings of the 16th conference on Innovative applications of artifical intelligence
Efficient solution algorithms for factored MDPs
Journal of Artificial Intelligence Research
Decentralized control of cooperative systems: categorization and complexity analysis
Journal of Artificial Intelligence Research
Solving transition independent decentralized Markov decision processes
Journal of Artificial Intelligence Research
Resource allocation among agents with MDP-induced preferences
Journal of Artificial Intelligence Research
On the use of integer programming models in AI planning
IJCAI'99 Proceedings of the 16th international joint conference on Artifical intelligence - Volume 1
Solving factored MDPs via non-homogeneous partitioning
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Decomposition techniques for planning in stochastic domains
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Flexible decomposition algorithms for weakly coupled Markov decision problems
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Iterative MILP methods for vehicle-control problems
IEEE Transactions on Robotics
A tutorial on decomposition methods for network utility maximization
IEEE Journal on Selected Areas in Communications
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Because an agent's resources dictate what actions it can possibly take, it should plan which resources it holds over time carefully, considering its inherent limitations (such as power or payload restrictions), the competing needs of other agents for the same resources, and the stochastic nature of the environment. Such agents can, in general, achieve more of their objectives if they can use -- and even create -- opportunities to change which resources they hold at various times. Driven by resource constraints, the agents could break their overall missions into an optimal series of phases, optimally reconfiguring their resources at each phase, and optimally using their assigned resources in each phase, given their knowledge of the stochastic environment. In this paper, we formally define and analyze this constrained, sequential optimization problem in both the single-agent and multi-agent contexts. We present a family of mixed integer linear programming (MILP) formulations of this problem that can optimally create phases (when phases are not predefined) accounting for costs and limitations in phase creation. Because our formulations simultaneously also find the optimal allocations of resources at each phase and the optimal policies for using the allocated resources at each phase, they exploit structure across these coupled problems. This allows them to find solutions significantly faster (orders of magnitude faster in larger problems) than alternative solution techniques, as we demonstrate empirically.