SIZZLE: A Knowledge-Acquisition Tool Specialized for the Sizing Task

  • Daniel Offutt
Part of the The Kluwer International Series in Engineering and Computer Science book series (SECS, volume 57)


SIZZLE is a prototype knowledge-acquisition tool for building sizers: expert systems that solve sizing problems. SIZZLE uses an extrapolate­from-a-similar-case problem-solving method. Using this strategy, a sizer produces a solution by first becoming reminded of a source sizing case similar to a target sizing problem to be solved, and then adjusting the solution of the source case to account for the differences between the source and the target. The problem-solving strategy assumed by SIZZLE makes strong assumptions about the problem domain. SIZZLE assumes that knowledge about sizing can be organized as a collection of validated cases (each case is a problem-description/solution pair) and that similarities among problem descriptions imply similarities among solutions.


Expert System Disk Space Resource Demand Physical Memory Sales Representative 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Kluwer Academic Publishers 1988

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  • Daniel Offutt

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