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Lookup NU author(s): Professor Alexander RomanovskyORCiD
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0).
© 2017, Springer International Publishing AG. There has been much focus on applying graphical techniques to analyse various kinds of structural errors in knowledge bases as a method of verification and reliability estimation. The most commonly applied technique has been Petri nets, or variations thereof, in achieving this objective with much success. However, although this approach has been considerably useful for verifying rules in earlier generations of knowledge-based systems, it is unclear if this approach can continue to be as useful, or indeed accessible, for verifying current or later generations of KBS, which have significantly larger, more complex, probabilistic rule sets. It has recently been argued that stochastic Petri nets can be successfully applied to continue with knowledge base verification, although, this method has required extensive and complex modifications that has led into proposals for fuzzy Petri nets. It is the view of this paper that the stochastic activity network formalism can provide a potentially useful alternative for the verification of fuzzy rule sets and can be more efficient and effective than complex derivatives of Petri nets. We present a high-level discussion of how this approach could be applied and used to analyse knowledge bases in ensuring that there are free of structural errors.
Author(s): Martin L, Romanovsky A
Publication type: Conference Proceedings (inc. Abstract)
Publication status: Published
Conference Name: International Workshop on Software Engineering for Resilient Systems (SERENE 2017)
Year of Conference: 2017
Print publication date: 12/08/2017
Online publication date: 11/08/2017
Acceptance date: 02/04/2016
Date deposited: 05/11/2017
Publisher: Springer Verlag
Library holdings: Search Newcastle University Library for this item
Series Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)