1. Sajjad Siddiqi and Jinbo Huang National ICT Australia and Australian National University Hierarchical Diagnosis of Multiple Faults

2. Consistency-based Diagnosis C D A Y X B Abnormal observation  :  A  B   D Which gate(s) are broken?

3. Consistency-based Diagnosis C D A Y X B System model  : okX  (A   C) okY  (B  C)  D Health variables: okX, okY Observables: A, B, D Nonobservable: C

4. Consistency-based Diagnosis C D A Y X B Abnormal observation  :  A  B   D Find values of (okX, okY) consistent with    : (0, 0), (0, 1), (1, 0) System model  : okX  (A   C) okY  (B  C)  D

5. Consistency-based Diagnosis System model  over health variables (okX, okY, …) observables nonobservables Given observation , diagnosis is assignment to health variables consistent with    Consider minimum-cardinality diagnoses

6. Search-based Approach Search for diagnoses consistent with    Reduced to finding solutions to SAT instance Cardinality enforced by extra constraints Often restricted to single/double faults

7. System Model Compile Tractable Form QueryEvaluator Compilation-based Approach

8. Most work is done off-line On-line diagnosis is efficient Off-line work is amortized over multiple queries Can handle arbitrary cardinality Off-line compilation can be bottleneck Compilation-based Approach

9. DAG of nested and/or Conjuncts share no variable (decomposable) or and or and X3X3 X1X1 X2X2 Decomposable Negation Normal Form (DNNF)

10. C D A Y X B Observation:  A  B   D 10 0011 11 System model  : okX  (A   C) okY  (B  C)  D Diagnosis Using DNNF

11. C D A Y X B Observation:  A  B   D or  okX  okY System model  : okX  (A   C) okY  (B  C)  D Diagnosis Using DNNF

12. System Model Compile Tractable Form QueryEvaluator Bottleneck Compilation-based Approach

13. Requires a health variable for each component c1908 has 880 gates; basic encoding fails to compile New technique to reduce number of health variables Preserves soundness and completeness w.r.t. min- cardinality diagnoses Requires only 160 health variables for c1908

14. Hierarchical Diagnosis

17. Identifying Cones Gate G dominates gate X if any path from X to output of circuit contains G All gates dominated by G form a cone Dominators found by breath-first traversal of circuit Treat maximal cones as blackboxes

18. Abstraction of Circuit  C = {T, U, V, A, B, C}

19. Top-level Diagnosis Diagnosis: {A, B, C}

20. Diagnosis of Cone Need to set inputs/output of cone according to top- level diagnosis Rest is similar, but not a simple recursive call (to avoid redundancy) Once cone diagnoses found, global diagnoses obtained by substitution

21. Diagnosis of Cone Top-level diagnosis: {A, B, C} 3 diagnoses for cone A: {A}, {D}, {E} 3 global diagnoses by substitution: {A, B, C} {D, B, C} {E, B, C}

22. Soundness Top-level diagnoses have same cardinality. Substitutions do not alter cardinality (cones do not overlap). Remains to show that cardinality of these diagnoses, d, is smallest. Proof by contradiction: Suppose there is diagnosis |P| < d. Replace every gate in P with its highest dominator to obtain P’. P’ is a valid top-level diagnosis, contradicting soundness of baseline diagnoser

23. Completeness Need to show every min-cardinality diagnosis is found Given diagnosis P of min cardinality d, replace every gate in P with its highest dominator to obtain P’ P’ has cardinality d, and only mentions gates in top-level abstraction, and hence will be found by top-level diagnosis (by completeness of baseline diagnoser) P itself will be found by substitution (by completeness of cone diagnosis)

24. Experiments Use ISCAS85 circuits Observations (inputs/outputs) randomly generated Multiple instances per circuit Use tool from (Huang and Darwiche, 2005) as baseline diagnoser

25. Results

26. Conclusion New technique for compilation-based diagnosis to scale up Preserves soundness and completeness w.r.t. min- cardinality diagnoses For further scalability, hybrid of search and compilation is possible