Dynamic programming: deterministic and stochastic models
Dynamic programming: deterministic and stochastic models
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
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
Acting optimally in partially observable stochastic domains
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Ideal reformulation of belief networks
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
Minimizing response times in real time planning and search
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
The traveling salesman: computational solutions for TSP applications
The traveling salesman: computational solutions for TSP applications
Resource-bounded reasoning in intelligent systems
ACM Computing Surveys (CSUR) - Special issue: position statements on strategic directions in computing research
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
Communication decisions in multi-agent cooperation: model and experiments
Proceedings of the fifth international conference on Autonomous agents
Answering Cooperative Recursive Queries in Web Federated Databases
NGITS '02 Proceedings of the 5th International Workshop on Next Generation Information Technologies and Systems
Optimal Sequencing of Contract Algorithms
Annals of Mathematics and Artificial Intelligence
Dynamic Composition of Information Retrieval Techniques
Journal of Intelligent Information Systems
Handling duration uncertainty in meta-level control of progressive processing
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Multiple-goal heuristic search
Journal of Artificial Intelligence Research
Real-time problem-solving with contract algorithms
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Reactive control of dynamic progressive processing
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Algorithm runtime prediction: Methods & evaluation
Artificial Intelligence
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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. When the rate of improvement of solution quality is uncertain, monitoring the progress of the algorithm can improve the utility of the system. This paper introduces a technique for run-time monitoring of anytime algorithms that is sensitive to the variance of the algorithm's performance, the time-dependent utility of a solution, the ability of the run-time monitor to estimate the quality of the currently available solution, and the cost of monitoring. The paper examines the conditions under which the technique is optimal and demonstrates its applicability.