Reducing problem-solving variance to improve predictability
Communications of the ACM
Do the right thing: studies in limited rationality
Do the right thing: studies in limited rationality
Time-dependent utility and action under uncertainty
Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
Planning and control
Artificial Intelligence - Special issue on knowledge representation
Computation and action under bounded resources
Computation and action under bounded resources
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
Ideal reformulation of belief networks
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
Highest utility first search across multiple levels of stochastic design
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Efficient meta-level control in bounded-rational agents
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
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
Field review: Metacognition in computation: A selected research review
Artificial Intelligence
AnyOut: anytime outlier detection on streaming data
DASFAA'12 Proceedings of the 17th international conference on Database Systems for Advanced Applications - Volume Part I
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Anytime algorithms offer a tradeoff between solution quality and computation time that has proved useful in applying artificial intelligence techniques to time-critical problems. To exploit this tradeoff, a system must be able to determine the best time to stop deliberation and act on the currently available solution. If there is uncertainty about how much solution quality will improve with computation time, or about how the problem state may change after the start of the algorithm, monitoring the algorithm's progress and/or the problem state can make possible a better stopping decision and so improve the utility of the system. This paper analyzes the issues involved in run-time monitoring of anytime algorithms. It reviews previous work and casts the problem in a new framework from which some improved monitoring strategies emerge.