Principles of artificial intelligence
Principles of artificial intelligence
Application of game tree searching techniques to sequential pattern recognition
Communications of the ACM
Diagnosing faults through responsibility
ACM '85 Proceedings of the 1985 ACM annual conference on The range of computing : mid-80's perspective: mid-80's perspective
Fault diagnosis through responsibility
IJCAI'85 Proceedings of the 9th international joint conference on Artificial intelligence - Volume 1
| Hi-index | 0.00 |
1N-ATE is an on-going project aimed at developing expert consultant systems for guiding a novice technician through each step of an electronics troubleshooting session. One goal of the project is to automatically produce, given a set of initial symptoms, a binary (pass/fail) decision tree of testpoints to be checked by the technician. This paper discusses our initial approach using a modified game tree search technique, the gamma miniaverage method. One of the parameters which guides this search technique - the cost of each test - is stored a priori The two other parameters that guide it - the conditional probability of test outcomes and the proximity to a solution - are provided by a dynamic model of an expert troubleshooter's beliefs about what in the device is good and what is bad. This model of beliefs is updated using probabilistic "tp.st-resuLt plausible-consequences" rules These rules are either provided by an expert technician, or approximated by a model-guided Rule Generator The model that guides the generation of rules is a simple block diagram of the Unit Under Test (UUT) augmented with component failure rates.