Toward reducing human involvement in validation of knowledge-based systems

    Authors

    R. Knauf; S. Tsuruta;A. J. Gonzalez

    Comments

    Authors: contact us about adding a copy of your work at STARS@ucf.edu

    Abbreviated Journal Title

    IEEE Trans. Syst. Man Cybern. Paart A-Syst. Hum.

    Keywords

    knowledge-based systems (KBSs); systems; validation knowledge; validation of artificial intelligence (AI) systems; RULE-BASED SYSTEMS; INTELLIGENT SYSTEMS; VERIFICATION; EXPERTS; ACQUISITION; FRAMEWORK; Computer Science, Cybernetics; Computer Science, Theory & Methods

    Abstract

    Human experts employed in validation exercises for knowledge-based systems (KBSs) often have limited time and availability. Furthermore, they often have different opinions from each other as well as from themselves over time. We address this situation by introducing the use of validation knowledge used in prior validation exercises for the same KBS. We present a validation knowledge base (VKB) that is the collective best experience of several human experts. The VKB is constructed and maintained across various validation exercises, and its primary benefits are given as follows: 1) more reliable validation results by incorporating external knowledge and 2) decrease of the experts' workload. We also present the concept of validation expert software agents (VESAs), which represent a particular expert's knowledge. VESA is a software agent corresponding to a specific human expert. It models the validation knowledge and behavior of its human counterpart by analyzing similarities with the responses of other experts. After a learning period, it can be used to temporarily substitute for its corresponding human expert. We also describe experiments with a small prototype system to evaluate the usefulness of these concepts.

    Journal Title

    Ieee Transactions on Systems Man and Cybernetics Part a-Systems and Humans

    Volume

    37

    Issue/Number

    1

    Publication Date

    1-1-2007

    Document Type

    Article

    Language

    English

    First Page

    120

    Last Page

    131

    WOS Identifier

    WOS:000244263700011

    ISSN

    1083-4427

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