World modeling for the dynamic construction of real-time control plans
Artificial Intelligence
Combinatorial optimization
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
Constrained Discounted Markov Decision Processes and Hamiltonian Cycles
Mathematics of Operations Research
On the Use of Integer Programming Models in AI Planning
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Constructing optimal policies for agents with constrained architectures
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Integer optimization models of AI planning problems
The Knowledge Engineering Review
Journal of Artificial Intelligence Research
Hierarchical solution of Markov decision processes using macro-actions
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Flexible decomposition algorithms for weakly coupled Markov decision problems
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Mathematical programming for deliberation scheduling in time-limited domains
AAMAS '06 Proceedings of the fifth 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
Resource allocation among agents with MDP-induced preferences
Journal of Artificial Intelligence Research
Resource-driven mission-phasing techniques for constrained agents in stochastic environments
Journal of Artificial Intelligence Research
An efficient resource allocation approach in real-time stochastic environment
AI'06 Proceedings of the 19th international conference on Advances in Artificial Intelligence: Canadian Society for Computational Studies of Intelligence
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A constrained agent is limited in the actions that it can take at any given time, and a challenging problem is to design policies for such agents to do the best they can despite their limitations. One way of improving agent performance is to break larger tasks into phases, where the constrained agent is better able to handle each phase and can reconfigure its limited capabilities differently for each phase. In this paper, we present algorithms for automating the process of finding and using mission phases for constrained agents. We analyze several variations of this problem that correspond to different classes of important constrained-agent problems, and show through analysis and experiments that our techniques can increase an agent's rewards for varying levels of constraints on the agent and on the phases.