Joshua Grass
Abstract
Anytime Algorithms are algorithms that exchange execution time for quality of results. Since many computational tasks are too complicated to be completed at real-time speeds, anytime algorithms allow systems to intelligently allocate computational time resources in the most effective way, depending on the current environment and the system's goals. This article briefly covers the motivations for creating anytime algorithms, the history of their development, a definition of anytime algorithms, and current research involving anytime algorithms.
- 1 Boddy, M. and Dean, T.L. Solving time-dependent planning problems. In Proceedings of the Eleventh International Joint Conference on Artificial Intelligence, pp. 979-984, Detroit, Michigan, 1989.Google Scholar
- 2 Dean, T.L. Intractability and time-dependent planning. In Proceedings of the 1986 Workshop on Reasoning about Actions and Plans, M.P. Georgeff and A.L. Lansky (eds.), Los Altos, California: Morgan Kaufmann, 1987.Google ScholarCross Ref
- 3 Dean, T.L. and Boddy, M. An analysis of time-dependent planning. In Proceedings of the Seventh National Conference on Artificial Intelligence, pp. 49-54, Minneapolis, Minnesota, 1988.Google ScholarDigital Library
- 4 Grass, J. and Zilberstein, S. Programming with anytime algorithms. In Proceedings of the IJCAI-95 Workshop on Anytime Algorithms and Deliberation Scheduling, Montreal, Canada, 1995.Google Scholar
- 5 Grass, J. and Zilberstein, S. Anytime Algorithm Development Tools. Technical report 95-94, University of Massachusetts at Amherst, 1995. Google ScholarDigital Library
- 6 Hansen, E. and Zilberstein, S. Monitoring the Progress of Anytime Problem-solving.Google Scholar
- 7 Horvitz, E.J. Reasoning about beliefs and actions under computational resource constraints. In Proceedings of the 1987 Workshop on Uncertainty in Artificial Intelligence, Seattle, Washington, 1987.Google Scholar
- 8 Horvitz, E.J., Suermondt, H.J., and Cooper, G.F. Bounded Conditioning: Flexible inference for decision under scarce resources. In Proceedings of the 1989 Workshop on Uncertainty in Artificial Intelligence, pp. 182-193, Windsor, Ontario, 1989.Google Scholar
- 9 Pos, A. Time-Constrained Model-Based Diagnosis. Master Thesis, Department of Computer Science, University of Twente, The Netherlands, 1993.Google Scholar
- 10 Russel, S.J. and Zilberstein, S. Composing real-time systems. In Proceedings of the Twelfth International Joint Conference on Artificial Intelligence, pp. 212-217, Sydney, Australia, 1991.Google Scholar
- 11 Sedgewick, Robert. Algorithms in C. Reading, Massachusetts: Addison-Wesley Publishing Co., 1990. Google ScholarDigital Library
- 12 Wellman, M.P. and Liu, C.L. State-Space Abstraction for Anytime Evaluation of Probabilistic Networks. In Proceedings of the 10th Conference on Uncertainty in AI, Seattle, WA, 1994.Google ScholarCross Ref
- 13 Zilberstein, S. Operational Rationality through Compilation of Anytime Algorithms. Ph.D. dissertation, Computer Science Division, University of California at Berkley, 1993. Google ScholarDigital Library
- 14 Zilberstein, S. and Hansen, E. Modeling Performance Improvement using Markov Processes. Private Communication, 1995.Google Scholar
- 15 Zilberstein, S. and Russel, S.J. Anytime sensing, planning and action: A Practical model for robot control. In Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence, pp. 1402-1407, Chambery, France, 1993.Google Scholar
- 16 Zilberstein, S. and Russel, S.J. Optimal Composition of Real-Time Systems. Artificial Intelligence, forthcoming, 1995. Google ScholarDigital Library
Index Terms
- Reasoning about computational resource allocation
Recommendations
Competitive proportional resource allocation policy for computational grid
Special issue: Computational science of lattice Boltzmann modellingThis paper presents a competitive proportional resource allocation in computational grid. A system model is described that allows agents representing various grid resources, which owned by different real world enterprises, to coordinate their resource ...
Resource reconstruction algorithms for on-demand allocation in virtual computing resource pool
Resource reconstruction algorithms are studied in this paper to solve the problem of resource on-demand allocation and improve the efficiency of resource utilization in virtual computing resource pool. Based on the idea of resource virtualization and ...
Optimizing cloud resource allocation: A long short-term memory and DForest-based load balancing approach
Load balancing is an element that must exist for a cloud server to function properly. Without it, there would be substantial lag and the server won’t be able to operate as intended. In a Cloud computing (CC) establishing, load balancing is the process of ...
Comments