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ABSTRACT
Anytime algorithms are playing an increasingly important role in the construction of effective reasoning and planning systems. Early work on anytime algorithms concentrated on the construction of applications in such areas as medical diagnosis and mobile robot navigation. In this paper we describe a programming environment to support the development of such applications as well as larger applications in which several anytime algorithms are used. The widespread use of anytime algorithms depends largely on the availability of such programming tools for algorithm construction, performance measurement, composition of anytime algorithms, and monitoring of their execution. We present a prototype system that meets these needs. Created in lisp, this library of functions, graphical tools and monitoring modules will accelerate and simplify the process of programming with anytime algorithms.
REFERENCES
Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.
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CITED BY 6
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Xiaopeng Xi , Eamonn Keogh , Christian Shelton , Li Wei , Chotirat Ann Ratanamahatana, Fast time series classification using numerosity reduction, Proceedings of the 23rd international conference on Machine learning, p.1033-1040, June 25-29, 2006, Pittsburgh, Pennsylvania
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