Expert Systems: Uncertainty Management Flashcards

1
Q

Examples of a source of uncertainty

A

Weak implications, Imprecise language, Unknown data, Combining the views of different experts

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2
Q

The difference between probability and fuzzy membership in measuring uncertainty

A

The probability of an event is the proportion of cases in which the event occurs. It’s a scientific measure of chance. It can be expressed mathematically as a numerical index with a range between 0 to unity. Fuzzy logic is determined as a set of mathematical principles for knowledge representation based on the degree of membership rather than on crisp membership of classical binary logic

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3
Q

What is uncertainty in AI?

A

The lack of exact knowledge that would enable us to reach a perfectly reliable solution

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4
Q

Weak implications as a source of uncertainty

A

They are vague associations between IF and THEN parts of the rules.

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5
Q

Examples of weak implications

A

If there are any certainty factors to indicate a degree of correlation or if there are any further reasoning based on the fact stated

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6
Q

Imprecise language as a source of uncertainty

A

There are various ways to express knowledge in the precise IF-THEN form of rules

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7
Q

Unknown data as a source of uncertainty

A

When data are incomplete or missing, the only solution is to accept the value “unknown” and proceed to an approximate reasoning with this value

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8
Q

Combining views as a source of uncertainty

A

Experts often have contradictory opinions and produce conflicting rules. To resolve the conflict, one has to attach a weight to each expert and then calculate the composite conclusion. No systematic method exists to obtain weights. Experts will reach exactly the same conclusions

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9
Q

How to calculate the probability of success?

A

The number of successes / The number of possible outcomes

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10
Q

How to calculate the probability of failure?

A

The number of failures / The number of possible outcomes

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11
Q

Crisp set (logic) vs Fuzzy set (logic)

A

Crisp set defines value either 1 or 0. It is also called a classical set and it shows full membership meaning true/false, yes/no, 0/1. Fuzzy set defines values between 0 and 1. It specifies the degree to which something is true and it shows partial membership meaning true to false, yes to no, 0 to 1

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