1. 22 March 1999. AAAI Symposium on AI in Equipment Maintenance Service & Support 1 Acquisition and Transformation of Likelihoods to Conditional Probabilities for Bayesian Networks or A new method for acquiring probabilities from domain experts when the causes are structured as a tree Claus Skaanning, Finn V. Jensen, Uffe Kjærulff and Anders Madsen Hewlett-Packard and Aalborg University, Denmark

2. 22 March 1999. AAAI Symposium on AI in Equipment Maintenance Service & Support 2 Background Troubleshooting applications Bayesian networks Breese & Heckerman (1996) The knowledge acquisition bottleneck

3. 22 March 1999. AAAI Symposium on AI in Equipment Maintenance Service & Support 3 Troubleshooting N components may be broken, c 1, …, c N Actions and questions associated with the causes Guide the user through the best troubleshooting sequence

4. 22 March 1999. AAAI Symposium on AI in Equipment Maintenance Service & Support 4 Bayesian networks Problem Cause 1 Cause 2 Subcause 1 Subcause 2 Specify conditional probabilities of nodes given their parents Ability to insert evidence and get updated probabilities of other variables

5. 22 March 1999. AAAI Symposium on AI in Equipment Maintenance Service & Support 5 Breese & Heckerman (1996) k independent actions that can solve the problem, A 1, …, A k p i =p(A i =y|e) : probability of A i solving the problem C i : cost of performing A i Given evidence e, A * is the best action to perform

6. 22 March 1999. AAAI Symposium on AI in Equipment Maintenance Service & Support 6 Knowledge Acquisition Bottleneck Our application : thousands of prior probabilities Estimate prior probabilities of very rare events Traditional methods offer little help here...

7. 22 March 1999. AAAI Symposium on AI in Equipment Maintenance Service & Support 7 Application Troubleshooting of Hewlett-Packard printer systems, including surrounding pieces, network, software, etc. Classes of problems : dataflow problems (no or corrupt output), unexpected output, errorcodes and miscellaneous (incl. printer behavior) Several hundreds of problems with dozens of causes and subcauses Technical diagnosis - single fault assumption - cause tree

8. 22 March 1999. AAAI Symposium on AI in Equipment Maintenance Service & Support 8 Causes as a tree

9. 22 March 1999. AAAI Symposium on AI in Equipment Maintenance Service & Support 9 Traditional approach Problem Cause1 Cause2 Subcause1 Subcause2 Estimate p(P | C 1, C 2 ), p(C 1 | S 1, S 2 ), p(C 2 ), p(S 1 ), p(S 2 )

10. 22 March 1999. AAAI Symposium on AI in Equipment Maintenance Service & Support 10 New approach 0.4 0.6 0.8 0.2 0.1 Problem Cause1 Cause2 Subcause1 Subcause2 Estimate p(P), p(C 1 | P), p(C 2 | P), p(S 1 | C 1 ), p(S 2 | C 1 ) Hinges on the single-fault assumption and troubleshooting experts vs printer experts

11. 22 March 1999. AAAI Symposium on AI in Equipment Maintenance Service & Support 11 Benefits of new approach Much more information / larger context for probability elicitation Ability to rank small set of causes against each other - sums to 100% - easier to get the overview If you get no output from the printer what is the probability that the printer parallel cable is defective? (out of hundreds of other causes) With three inexperienced domain experts we elicited 2000 probabilities in one week  1-2 probabilities / minute Easier and less painful to elicit probabilities  better quality probabilities Fewer discussions  quicker elicitation ?

12. 22 March 1999. AAAI Symposium on AI in Equipment Maintenance Service & Support 12 Implementation of likelihoods Constraints Problem Cause1 Cause2 Subcause1 Subcause2 Constraint1 Constraint2 0.1 yes 0.9 no 0.04 yes 0.96 no 0.06 yes 0.94 no 0.008 yes 0.992 no 1.0 on 0.0 off 1.0 on 0.0 off 0.032 yes 0.968 no

13. 22 March 1999. AAAI Symposium on AI in Equipment Maintenance Service & Support 13 Implementation Problem Cause1 Cause2 Subcause1 Subcause2 Constraint1 Constraint2 z x 1-x y 1-y

14. 22 March 1999. AAAI Symposium on AI in Equipment Maintenance Service & Support 14 Benefits of constraints Causal direction is maintained This is necessary in some situations where causal relations are used for, e.g., persistence Which past evidence persists when I replace this component? Ability to remove single-fault assumption and replace it with an assumption of independent faults

15. 22 March 1999. AAAI Symposium on AI in Equipment Maintenance Service & Support 15 Implementation as chain graph Problem Cause 1 Cause 2 Subcause 1 Subcause 2 x 1-x z y 1-y

16. 22 March 1999. AAAI Symposium on AI in Equipment Maintenance Service & Support 16 Implementation States enforce single-fault assumption

17. 22 March 1999. AAAI Symposium on AI in Equipment Maintenance Service & Support 17 Implementation A single node Causes Mutually exclusive states enforce single-fault assumption

18. 22 March 1999. AAAI Symposium on AI in Equipment Maintenance Service & Support 18 Extensions Lifting the single-fault assumption in specific cases Lifting the single-fault assumption in general - enforcing independence assumption Representing remainder of network - actions and questions