Formulation of tradeoffs in planning under uncertainty
Formulation of tradeoffs in planning under uncertainty
Proceedings of the first international conference on Principles of knowledge representation and reasoning
Deliberation scheduling for problem solving in time-constrained environments
Artificial Intelligence
Operational rationality through compilation of anytime algorithms
Operational rationality through compilation of anytime algorithms
Optimal composition of real-time systems
Artificial Intelligence
Monitoring and control of anytime algorithms: a dynamic programming approach
Artificial Intelligence - special issue on computational tradeoffs under bounded resources
The Complexity of Decentralized Control of Markov Decision Processes
Mathematics of Operations Research
The control of reasoning in resource-bounded agents
The Knowledge Engineering Review
Meta-Level Reasoning in Deliberative Agents
IAT '04 Proceedings of the IEEE/WIC/ACM International Conference on Intelligent Agent Technology
Heuristic search value iteration for POMDPs
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
A framework for meta-level control in multi-agent systems
Autonomous Agents and Multi-Agent Systems
Decentralized control of cooperative systems: categorization and complexity analysis
Journal of Artificial Intelligence Research
Provably bounded optimal agents
IJCAI'93 Proceedings of the 13th international joint conference on Artifical intelligence - Volume 1
A bilinear programming approach for multiagent planning
Journal of Artificial Intelligence Research
Multiagent Meta-level Control for a Network of Weather Radars
WI-IAT '10 Proceedings of the 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 02
Metareasoning: Thinking about Thinking
Metareasoning: Thinking about Thinking
Multiagent meta-level control for radar coordination
Web Intelligence and Agent Systems
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Anytime algorithms allow a system to trade solution quality for computation time. In previous work, monitoring techniques have been developed to allow agents to stop the computation at the "right" time so as to optimize a given time-dependent utility function. However, these results apply only to the single-agent case. In this paper we analyze the problems that arise when several agents solve components of a larger problem, each using an anytime algorithm. Monitoring in this case is more challenging as each agent is uncertain about the progress made so far by the others. We develop a formal framework for decentralized monitoring, establish the complexity of several interesting variants of the problem, and propose solution techniques for each one. Finally, we show that the framework can be applied to decentralized flow and planning problems.