Empirical methods for artificial intelligence
Empirical methods for artificial intelligence
Evaluation of verification tools for knowledge-based systems
International Journal of Human-Computer Studies
Evaluating knowledge engineering techniques
International Journal of Human-Computer Studies
International Journal of Human-Computer Studies
Imprecise and Approximate Computation
Imprecise and Approximate Computation
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
NORA/HAMMR: making deduction-based software component retrieval practical
ASE '97 Proceedings of the 12th international conference on Automated software engineering (formerly: KBSE)
Towards Robust Collaborative Filtering
AICS '02 Proceedings of the 13th Irish International Conference on Artificial Intelligence and Cognitive Science
Promoting Recommendations: An Attack on Collaborative Filtering
DEXA '02 Proceedings of the 13th International Conference on Database and Expert Systems Applications
The engineering of expert systems testing process
AIC'07 Proceedings of the 7th Conference on 7th WSEAS International Conference on Applied Informatics and Communications - Volume 7
A systematic review of software robustness
Information and Software Technology
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The overall aim of this paper is to provide a general setting for quantitative quality measures of Knowledge-Based System behavior which is widely applicable to many Knowledge-Based Systems. We propose a general approach that we call "degradation studies": an analysis of how system output degrades as a function of degrading system input, such as incomplete or incorrect inputs. Such degradation studies avoid a number of problems that have plagued earlier attempts at defining such quality measures because they do not require a comparison between different (and often incomparable) systems, and they are entirely independent of the internal workings of the particular Knowledge-Based System at hand. To show the feasibility of our approach, we have applied it in a specific case-study. We have taken a large and realistic vegetation-classification system, and have analyzed its behavior under various varieties of missing input. This case-study shows that degradation studies can reveal interesting and surprising properties of the system under study.