Davide is a research scientist at the University of Bologna (which has the distinction of being the oldest continuously operating university in the world and a wonderful place).
Davide started his presentation with well known story by J. C. Bezdek to illustrate the challenges of reasoning with fuzzy – or imperfect – information (a well known story that unfortunately nobody in the audience seemed to know ;)). The key challenges are well known – we are faced with:
- lack of information
- ill defined information
- erroneous information
Davide explains the choice of “imperfection” rather than “uncertainty” as the term to use to denote these cases. Imperfection is in essence the opposite of perfect precision (of course), but dealing with it brings us immediately out of the scope of what traditional rules engines do. Furthermore, removing imperfection from the picture by ignoring it, simplifying it, etc, essentially leads to ignoring essential information. The characteristics of the imperfection are relevant data for writing rules.
Davide’s work focuses on creating more robust systems that may be applicable to imperfect information and still leverage rule based systems. Using a modus ponens model, he illustrates the impact of introducing imperfection to the way the rules engines work. In essence, we need to think about the connectors (combination of premises, logical implication, etc…) differently, solve conflicts created by the imperfection and handle missing value. Davide would like to preserve the structure of rules, and hide those complexities from the rules expression itself.
Davide contrasted the frequentist approach, to the Bayesian approach, to finally the Fuzzy logic approach, to deal with imperfection. Each one of these approaches has its applicability domain, as well as its philosophical foundations. Davide then went into a few details on the approaches – I will refer you to the slides for the information (lack of scientific notation capabilities in this blog writer is part of it…).
The key lesson Davide communicates is that you should not forget imperfection when creating and managing rules.
Charles Young asked what I think is a very good question: why is it that while in the early days rules solutions included Bayesian capabilities or equivalent analytics capability, they no longer include them?
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