Learning about Software Errors Via Systematic Experimentation

Terrance Goan, Oren Etzioni

Classical planners assume that their internal model is both correct and complete. The dynamic nature of realworld domains (e.g., multi-user software environments) makes these assumptions untenable. Several new planners (e.g.,XII [2]) have been designed to work with incomplete information, and strides have been made in planning with potentially incorrect information. But, efficient operation in the presence of incorrect information is highly dependent on a planner’s ability to detect errors. Failing to recognize errors can result in unexpected and potentially destructive effects, as well as further corruption of the world model.

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