Model-Based Probabilistic Reasoning for Electronics Troubleshooting

Abstract

IN-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 gam.ma 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 "test"T'esult .... 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.