Stochastic activity networks for the verification of knowledge bases

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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.




Martin, L., & Romanovsky, A. (2017). Stochastic activity networks for the verification of knowledge bases. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10479 LNCS, pp. 37–44). Springer Verlag.

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