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Special issue on computational tradeoffs under bounded resources

✍ Scribed by Eric Horvitz; Shlomo Zilberstein

Publisher
Elsevier Science
Year
1998
Tongue
English
Weight
126 KB
Volume
100
Category
Article
ISSN
0004-3702

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πŸ“œ SIMILAR VOLUMES

Special issue on computational tradeoffs
Special issue on computational tradeoffs under bounded resources
πŸ“‚ Article πŸ“… 1998 πŸ› Elsevier Science 🌐 English βš– 134 KB

Over the last decade, AI researchers have investigated flexible inferential procedures and representations that allow reasoning systems to gracefully trade off one or more dimensions of the quality of inferred results for the quantity of time or memory required to generate the results. Methods for m

Special issue on computational tradeoffs
Special issue on computational tradeoffs under bounded resources
πŸ“‚ Article πŸ“… 1998 πŸ› Elsevier Science 🌐 English βš– 136 KB

Over the last decade, AI researchers have investigated flexible inferential procedures and representations that allow reasoning systems to gracefully trade off one or more dimensions of the quality of inferred results for the quantity of time or memory required to generate the results. Methods for m

Special issue on computational tradeoffs
Special issue on computational tradeoffs under bounded resources
✍ Eric Horvitz; Shlomo Zilberstein πŸ“‚ Article πŸ“… 1998 πŸ› Elsevier Science 🌐 English βš– 136 KB

Over the last decade, AI researchers have investigated flexible inferential procedures and representations that allow reasoning systems to gracefully trade off one or more dimensions of the quality of inferred results for the quantity of time or memory required to generate the results. Methods for m

Computational tradeoffs under bounded re
Computational tradeoffs under bounded resources
✍ Eric Horvitz; Shlomo Zilberstein πŸ“‚ Article πŸ“… 2001 πŸ› Elsevier Science 🌐 English βš– 39 KB

Over the nearly fifty years of research in Artificial Intelligence, investigators have continued to highlight the computational hardness of implementing core competencies associated with intelligence. Key pillars of AI, including search, constraint propagation, belief updating, learning, decision ma