Compiling diagnostic rules and redesign plans from a structure/behavior device model

Chapter. Knowledge-based Aided Design. Academic Press, 1992
Compiling diagnostic rules and redesign plans from a structure/behavior device model
Richard Keller, Catherine Baudin, Yimi Ywasaki, P. Nayak, Kazuo Tanaka

The current generation of expert systems is fueled by special-purpose, task-specific associational rules developed with the aid of domain experts. In many cases, the expert has distilled or compiled these so-called 'shallow rules from 'deeper' models of the application domain in order to optimize task performance.

With the traditional knowledge engineering approach, only the shallow, special-purpose rules are elicited from the expert - not the underlying domain models upon which they are based. This results in two significant problems.

First, expert systems cannot share knowledge bases because they contain only special-purpose rules and lack the underlying general domain knowledge that applies across tasks. Second, because the underlying models are missing, shallow rules are unsupported and brittle.

This chapter describes a proposed second generation expert system architecture that addresses these problems by linking special-purpose rules to underlying domain models using a process called rule compilation. Rule compilation starts with a detailed domain model, and gradually incorporates various simplifying assumptions and approximations into the model, thereby producing a series of successively less general - but more task-efficient - models of the domain.

The end product of the rule compilation process is an associational rule model specialized for the task at hand.The process of rule compilation is illustrated with two simple implemented examples.

In the first, a structure/behavior model of a simple engineered device is compiled into a set of plans for redesign. In the second, the same underlying device model is compiled into a set of fault localization rules for troubleshooting.

Another publication from the same category: Machine Learning and Data Science

WWW '17 Perth Australia April 2017

Drawing Sound Conclusions from Noisy Judgments

David Goldberg, Andrew Trotman, Xiao Wang, Wei Min, Zongru Wan

The quality of a search engine is typically evaluated using hand-labeled data sets, where the labels indicate the relevance of documents to queries. Often the number of labels needed is too large to be created by the best annotators, and so less accurate labels (e.g. from crowdsourcing) must be used. This introduces errors in the labels, and thus errors in standard precision metrics (such as P@k and DCG); the lower the quality of the judge, the more errorful the labels, consequently the more inaccurate the metric. We introduce equations and algorithms that can adjust the metrics to the values they would have had if there were no annotation errors.

This is especially important when two search engines are compared by comparing their metrics. We give examples where one engine appeared to be statistically significantly better than the other, but the effect disappeared after the metrics were corrected for annotation error. In other words the evidence supporting a statistical difference was illusory, and caused by a failure to account for annotation error.