Abstract
A way to circumvent the awkwardness and constraints encountered while using Certainty Factors (CF) for modelling uncertainty in uncertain Knowledge Bases has been proposed. It is based on introducing Data Marks for askable conditions and Data Marks for conclusions of relational models, followed by choosing the best suited way to propagate those Data Marks into Data Marks of rule conclusions. This is done in a way orthogonal to the inference using Aristotelian Logic. Using Data Marks instead of Certainty Factors removes thus the intelectual discomfort caused by rejecting the Aristotelian law of excluded middle while using the CF methodology.
Paper Type
Event
Data Marks for Uncertainty Management in Expert System Knowledge Bases
A way to circumvent the awkwardness and constraints encountered while using Certainty Factors (CF) for modelling uncertainty in uncertain Knowledge Bases has been proposed. It is based on introducing Data Marks for askable conditions and Data Marks for conclusions of relational models, followed by choosing the best suited way to propagate those Data Marks into Data Marks of rule conclusions. This is done in a way orthogonal to the inference using Aristotelian Logic. Using Data Marks instead of Certainty Factors removes thus the intelectual discomfort caused by rejecting the Aristotelian law of excluded middle while using the CF methodology.
Recommended Citation
Niederliński, A. (2016). Data Marks for Uncertainty Management in Expert System Knowledge Bases. In J. Gołuchowski, M. Pańkowska, C. Barry, M. Lang, H. Linger, & C. Schneider (Eds.), Information Systems Development: Complexity in Information Systems Development (ISD2016 Proceedings). Katowice, Poland: University of Economics in Katowice. ISBN: 978-83-7875-307-0. http://aisel.aisnet.org/isd2014/proceedings2016/CreativitySupport/4.