3_chapter.pptx - BEU 5183 ARTIFICIAL INTELLIGENCE CHAPTER 3 Uncertainty Management In Rule Based Expert Systems W.R.W OMAR Uncertainty Management In

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BEU 5183 ARTIFICIAL INTELLIGENCE CHAPTER 3 Uncertainty Management In Rule Based Expert Systems W.R.W OMAR
Uncertainty Management In Rule Based Expert Systems 3.1 Introduction on what is uncertainty 3.2 Basic probability theory 3.3 Bayesian reasoning 3.4 FORECAST: Bayesian accumulation of evidence 3.5 Bias of the Bayesian method 3.6 Certainty factors theory and evidential reasoning 3.7 FORECAST: an application of certainty factors 3.8 Comparison of Bayesian reasoning and certainty Factors
Introduction on what is uncertainty An information about a problem may be incomplete, inconsistent and uncertain. Uncertainty–Lack of adequate information to make a decision. Uncertainty can be defined as the lack of exact know ledge that would enable to reach a perfect reliable conclusion. Uncertainty prevents us from making the best decisions or we may end up with a bad decision . We the humans are able to survive in this world filled with uncertainties. The reason is we are able to reason out.
In classical logic we have exact reasoning and it assumes that perfect knowledge always. IF A is True then A is not False. IF A is False then A is not True. To develop expert systems for real-world problems, we couldn’t provide exact perfect information. This is because the information available about the real-world problem is in exact, imprecise and uncertain
Four sources of uncertain knowledge in expert system Weak implications Imprecise Language Unknown data Combining the views of different experts’s opinions Weak Implications: Rule based system suffers with weak implications and vague association. Domain experts and knowledge engineer finds it very difficult to correlate the condition and action parts of the rules. The system must have the capability to accept vagueness association
Imprecise Language Our natural language is inherently ambiguous, vague, imprecise and filled with uncertainty. We describe facts using terms like often , sometimes , very often and it is ve ry difficult to express the knowledge precisely in the form of IF THEN production rules.

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