Expert System: Uncertainty & Bayseian Inference Flashcards
What is Uncertainty?
the lack of exact knowledge that would enable us to reach a perfectly reliable solution.
Name four sources of uncertainty.
Weak implications
Imprecise language
Unknown data
Combining the views of different experts
Describe how Weak implications can be a source of Uncertainty
In KBS, domain experts and knowledge engineers have the painful task
of establishing concrete correlations between IF (condition) and THEN (action) parts of the rules.
Therefore, KBS need to have the ability to handle vague associations, for example by accepting the degree of correlations as numerical certainty factors.
Describe how Imprecise language can be a source of uncertainty.
- Our natural language is ambiguous and imprecise.
- We describe facts with such terms as often and sometimes, frequently and hardly ever.
- As a result, it can be difficult to express knowledge in the precise IF-THEN form of production rules.
- However, if the meaning of the facts is quantified, it can be used in KBS.
Describe how Imprecise language can be a source of uncertainty.
When the data is incomplete or missing, the only solution is
to accept the value “unknown” and proceed to an approximate reasoning with
this value
Describe how Combining the views of different experts can be a source of Uncertainty.
- Large KBS usually combine the knowledge and expertise of a number of experts.
- Unfortunately, experts often have contradictory opinions and produce conflicting rules.
- To resolve the conflict, the knowledge engineer has to attach a weight to each expert and then calculate the composite conclusion.
What is a Certainty Factor?
Certainty factors theory is a popular alternative to Bayesian reasoning
rength of their belief in terms that were neither logical nor mathematically consisten, -> /unable to use a classical probability approach. Instead they decided to introduce a certainty factor (cf ), a number to measure the expert’s belief.
he maximum value of the certainty factor was +1.0 (definitely true) and the minimum -1.0 (definitely false
In expert systems with certainty factors, the knowledge base consists of a set of rules that have the following syntax:
IF <evidence></evidence>
THEN <hypothesis> {cf}</hypothesis>
The certainty factors theory is based on two functions: measure of belief MB(H,E), MD(H,E)
Which applications are most suitable for bayesian reasoning and which are for certainty factors?
Bayesian:
- where statistical data is usually available and accurate probability statements can be made.
- f. i. forecastin, planning
certainty factors
- reliable statistical information is not available or we cannot assume the conditional independence of evidence.
- diagnostics, particularly in medicine
What is the prior probability?
p(H) is the prior probability of hypothesis H being true;
What is the conditional probabiliyt?
The probability that event A will occur if event B occurs is called the conditional probability.
What it the joint probability?
The number of times A and B can occur, or the probability that both A and B will occur, is called the joint probability of A and B. It is represented mathematically as p(A ∩ B).
How does Bayesian reasoning work?
IF E is true
THEN H is true {with probability p}
This rule implies that if event E occurs, then the probability that event H will occur is p.
