Proceedings of the first international conference on Principles of knowledge representation and reasoning
Monitoring and control of anytime algorithms: a dynamic programming approach
Artificial Intelligence - special issue on computational tradeoffs under bounded resources
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Neuro-Dynamic Programming
Metacognition in computation: a selected research review
Artificial Intelligence
A framework for meta-level control in multi-agent systems
Autonomous Agents and Multi-Agent Systems
Solving large TÆMS problems efficiently by selective exploration and decomposition
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Using performance profile trees to improve deliberation control
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Definition and complexity of some basic metareasoning problems
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Managing online self-adaptation in real-time environments
IWSAS'01 Proceedings of the 2nd international conference on Self-adaptive software: applications
On the complexity of solving Markov decision problems
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Determining the value of information for collaborative multi-agent planning
Autonomous Agents and Multi-Agent Systems
Using conflict resolution to inform decentralized learning
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
Multiagent meta-level control for radar coordination
Web Intelligence and Agent Systems
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Meta-level control manages the allocation of limited resources to deliberative actions. This paper discusses efforts in adding meta-level control capabilities to a Markov Decision Process (MDP)-based scheduling agent. The agent's reasoning process involves continuous partial unrolling of the MDP state space and periodic reprioritization of the states to be expanded. The meta-level controller makes situation-specific decisions on when the agent should stop unrolling in order to derive a partial policy while bounding the costs of state reprioritization. The described approach uses performance profiling combined with multi-level strategies in its decision making. We present results showing the performance advantage of dynamic meta-level control for this complex agent.