• Rule based systems can be augmented to draw variably certain conclusions.
• The basic idea is to add Certainty Factors to rules, and use these to calculate the measure of belief in some hypothesis.
• Certainty Factors are related to conditional probability, but are not the same.
• A Certainty Factor involves a measure which represents a level of confidence that some condition is true.
• They consist of two components – A measure of belief (Step of opinion) and a measure of disbelief (Step of incredulity).
• In step of opinion, given the hypothesis (H) and evidence (E), measure of belief is the extent to which the evidence supports the hypothesis.
• In step of incredulity, given the disbelief hypothesis (H) and evidence (E), measure of disbelief is the extent to which the evidence supports the negative of hypothesis.
Certainty factor [Hypothesis, Extent]
=
Measure belief [Hypothesis, Extent] – Measure disbelief [Hypothesis, Extent]
CF in rule based systems:
• A rule based system can propagate CFs via rules, to give CFs to conclusions.
• For this, rules too may have CFs associated with them.
• Ex.:
RULE [Is the petrol tank empty?]
If [the result of trying the starter] = “The car cranks normally” and [a petrol smell] = “Not present when trying the starter”
Then [the petrol tank] = “empty” @90.
• This says that if we know that the 2 conditions in the premise hold, then this knowledge gives a degree of belief that [the petrol tank] has the value “empty”.
• If no CF is given for a rule, it is assumed to be 100%.
• The basic idea is to add Certainty Factors to rules, and use these to calculate the measure of belief in some hypothesis.
• Certainty Factors are related to conditional probability, but are not the same.
• A Certainty Factor involves a measure which represents a level of confidence that some condition is true.
• They consist of two components – A measure of belief (Step of opinion) and a measure of disbelief (Step of incredulity).
• In step of opinion, given the hypothesis (H) and evidence (E), measure of belief is the extent to which the evidence supports the hypothesis.
• In step of incredulity, given the disbelief hypothesis (H) and evidence (E), measure of disbelief is the extent to which the evidence supports the negative of hypothesis.
Certainty factor [Hypothesis, Extent]
=
Measure belief [Hypothesis, Extent] – Measure disbelief [Hypothesis, Extent]
CF in rule based systems:
• A rule based system can propagate CFs via rules, to give CFs to conclusions.
• For this, rules too may have CFs associated with them.
• Ex.:
RULE [Is the petrol tank empty?]
If [the result of trying the starter] = “The car cranks normally” and [a petrol smell] = “Not present when trying the starter”
Then [the petrol tank] = “empty” @90.
• This says that if we know that the 2 conditions in the premise hold, then this knowledge gives a degree of belief that [the petrol tank] has the value “empty”.
• If no CF is given for a rule, it is assumed to be 100%.
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