Normality and Faults in Logic-Based Diagnosis

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Presentation transcript:

Normality and Faults in Logic-Based Diagnosis Presented by Kelly Benson Mouiad Al-Wahah

Definitions we agreed upon.. Def 1. Diagnosis is the problem of trying to find what is wrong with some system based on knowledge about the design/structure of the system, possible malfunctions that can occur in the system and observations made of the behavior of the system. Def 2. An abductive diagnosis is finding a set of causes which can imply the observations. Def 3. Observation is the set of observations made of the actual artifacts we are diagnosing.

Definitions we agreed upon.. Def 4. Normality assumptions are hypotheses that some component is working properly. Def 5. Abnormality assumptions, the negation of def. 4 above. Def 6. Fault assumptions are assumptions of some fault or disease.

Knowledge base for Diagnosis  

 

Normality in Abduction Diagnosis A component is normal if it is being produced in a particular case. Battery example: Hypothesis: battOK(B, V), battAB(B, V) Ex: diagnoses entails observations

Normality in Abduction Diagnosis Battery example: Observation: volt(series(b1,b2), 1.456)

Faults in Abduction Diagnosis Replace abnormalities with fault assumptions Flat battery: 0.3 > V > 1.2 Shorted battery: V = 0 Observation : volt(series(b1,b2), 1.517)

Faults in Abduction Diagnosis, cont.. Diagnoses: Overcharged battery (1.6 < V < 2.0) Both batteries are OK, or one is overcharged and one is flat.

Faults evolution in Abduction Diagnosis No fault information Deals with abnormality (vagueness) as a fault.

Issues Representing observations Example: Argument 1: the input 4 and the output 16 are what we observed. Argument 2: we just observed the output 16.

Issues Noise We allow the hypothesis of error, Include the fact as observation Will get the fact:

Issues Hierarchical Reasoning If batteries were complex power stations, then abductive diagnosis can handle this by modeling the levels of abstraction.

Issues Efficiency Use backward chaining starting from observations. Prove negation of faults. Undecidable.

Issues  

Consistency-Based (CB) Diagnosis  

 

Normality in Consistency-Based Diagnosis A component is normal if it works correctly all the time. Battery example: - - Observations: volt(series(b1,b2), 1.456) - Hypotheses:

Normality in Consistency-Based Diagnosis ~ab(b1) and ~ab(b2) -We have two diagnoses: {ab(b1)}, {ab(b2)}

Faults in CB Diagnosis Assume normality and let faults be concluded as a side effect Assume the absence of faults and let normality be concluded as a side effect. Assume both normality and the absence of faults.

Faults in CB Diagnosis..cont Flat battery: 0.3 > V > 1.2 Shorted battery: V = 0 Assuming b1 is ok and observing volt(series(b1,b2))

Faults in CB Diagnosis..cont Assuming both batteries are ok rules out the possibility of both being flat: Must treat abnormality as a fault!

Faults in CB Diagnosis..cont Overcharged battery (1.6 < V < 2.0) Both batteries are OK, or one is overcharged and one is flat. The interpretation here is different than AD.

Faults evolution in CB diagnosis  

Faults evolution in CB diagnosis..cont Replace the complete knowledge assumption with:

Issues Representing observations Example: Argument 1: the input 4 and the output 16 are what we observed. Argument 2: we just observed the output 16. CB diagnosis requires the first form.

Issues Noise We allow the hypothesis of error, Include the fact as facts Will get the fact:

Issues Hierarchical Reasoning If batteries were complex power stations, then CB diagnosis can handle this by modeling the levels of abstraction.

Issues Efficiency Use forward chaining starting from observations. Continue until contradictions occur. Undecidable.

Issues Epistemological assumption CWA is strongly assumed. Unanticipated observations are ignored.

Conclusions No one true logical definition of diagnosis Both AB and CB diagnosis approaches need different KR. Both AB and CB diagnosis approaches can work in real-valued domains.

Bibliography David Poole, Representing Knowledge for Logic-Based Diagnosis David Poole, Normality and Faults in Logic-Based Diagnosis Andrew Smith, Jhih-Rong Lin, Jeremy Lewis, Slides titled Consistency-Based vs. Explanation-Based (Abduction) Diagnosis