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Uncertainty in Expert Systems (Certainty Factors)

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Certainty factor (cf) First introduced by MYCIN A measure of an expert’s belief in a fact or rule. Ranges from 1.0(definitely true) to -1.0 (definitely false).

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Certainty factor (cf) “Fuzzy” reasoning about probability.

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Use of CFs CFs capture the certainty that a given rule holds. For example: o IF animal lays eggs THEN animal is bird {cf 0.3} o IF animal lays eggs THEN animal is reptile {cf 0.2}

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Use of CFs IF animal lays eggs THEN animal is bird {cf 0.3} animal is reptile {cf 0.2} Note that CFs don’t need to add up to 1: leftover is “other”.

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Use of CFs CFs are also applied to evidence o animal lays eggs {cf 0.6} o temperature is high {cf 0.9} o user likes red wine {cf 0} (i.e. user has never tried red wine) Represents reliability or availability of evidence. Typically given by the user at run time.

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Propagation of CFs For a single antecedent rule: o cf(E) is the certainty factor of the evidence. o cf(R) is the certainty factor of the rule.

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Single antecedent rule example IF patient has toothache THEN problem is cavity {cf 0.3} Patient has toothache {cf 0.9} What is the cf(cavity, toothache)?

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Propagation of CFs (multiple antecedents) For conjunctive rules: o IF AND... AND THEN {cf} For two evidences E1 and E2: o cf(E1 AND E2) = min(cf(E1), cf(E2))

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Conjunctive example IF patient has toothache AND patient has prior cavities THEN problem is cavity {cf 0.3} Patient has toothache {cf -0.5} Patient has prior cavities {cf 0.9} What is the cf for “problem is cavity”?

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Propagation of CFs (multiple antecedents) For disjunctive rules: o IF OR... OR THEN {cf} For two evidences E1 and E2: o cf(E1 OR E2) = max(cf(E1), cf(E2))

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Disjunctive example IF patient has toothache OR patient has prior cavities THEN problem is cavity {cf 0.3} Patient has toothache {cf -0.5} Patient has prior cavities {cf 0.9} What is the cf for “problem is cavity”?

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Exercise IF (P1 AND P2) OR P3 THEN C1 (0.7) AND C2 (0.3) Assume cf(P1) = 0.6, cf(P2) = 0.4, cf(P3) = 0.2 What is cf(C1), cf(C2)?

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Multiple rules affecting H If the hypothesis is affected by several rules: Rule 1 : IF A is X THEN C is Z {cf 0.8} Rule 2 : IF B is Y THEN C is Z {cf 0.6}

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Multiple rules example: IF patient has toothache THEN problem is cavity {cf 0.3} IF patient has prior cavities THEN problem is cavity {cf 0.7} Patient has toothache (cf –0.5) Patient has prior cavities (0.9) Calculate cf for “problem is cavity”.

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Bayesian vs. certainty factors Probability theory is ‘good math’, and works well if statistical data is available and accurate probability statements can be made. CF theory lacks formal mathematical foundation, but better able the kind of estimates an expert is likely to make of probability.

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1 Knowledge Based Systems (CM0377) Lecture 11 (Last modified 26th March 2001)

1 Knowledge Based Systems (CM0377) Lecture 11 (Last modified 26th March 2001)

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