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Validation and verification of knowledge-based systems: report on EUROVAV99

  • FRANS COENEN (a1), TREVOR BENCH-CAPON (a1), ROBIN BOSWELL (a2), JULIETTE DIBIE-BARTHÉLEMY (a3), BARRY EAGLESTONE (a4), RIK GERRITS (a5), ERIC GRÉGOIRE (a6), ANTONI LIGE¸ZA (a7), LUIS LAITA (a8), MIECZYSLAW OWOC (a9), FLORENCE SELLINI (a10), SILVIE SPREEUWENBERG (a5), JAN VANTHIENEN (a11), ANCA VERMESAN (a12) and NIRMALIE WIRATUNGA (a2)...

Abstract

Knowledge-Based (KB) technology is being applied to complex problem solving and safety and business critical tasks in many application domains. Concerns have naturally arisen as to the dependability of Knowledge-Based Systems (KBS). As with any software, attention to quality and safety must be paid throughout development of a KBS, and rigorous Verification and Validation (V&V) techniques must be employed. Research in V&V of KBSs has emerged as a distinct field only in the last decade, and is intended to address issues associated with quality and safety aspects of KBSs, and to provide such applications with the same degree of dependability as conventional applications. In recent years, V&V of KBSs has been the topic of annual workshops associated with the main AI conferences, such as AAAI, IJCAI and ECAI.

Knowledge-Based (KB) technology is being applied to complex problem solving and safety and business critical tasks in many application domains. Concerns have naturally arisen as to the dependability of Knowledge-Based Systems (KBS). As with any software, attention to quality and safety must be paid throughout development of a KBS, and rigorous Verification and Validation (V&V) techniques must be employed. Research in V&V of KBSs has emerged as a distinct field only in the last decade, and is intended to address issues associated with quality and safety aspects of KBSs, and to provide such applications with the same degree of dependability as conventional applications. In recent years, V&V of KBSs has been the topic of annual workshops associated with the main AI conferences, such as AAAI, IJCAI and ECAI.

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The Knowledge Engineering Review
  • ISSN: 0269-8889
  • EISSN: 1469-8005
  • URL: /core/journals/knowledge-engineering-review
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