---
title: "Model-Based Probabilistic Reasoning for Electronics Troubleshooting"
authors:
  - "Richard R. Cantone"
  - "Frank J. Pipitone"
  - "W. Brent Lander"
  - "Michael P. Marrone"
doi: ""
url: "https://ijcai.org/proceedings/1983/49"
pdf: "https://ijcai.org/proceedings/1983/0049.pdf"
venue: "Proceedings of the Eighth International Joint Conference on Artificial Intelligence"
year: 1983
pages: "207-210"
track: "Expert Systems"
keywords:
---

# Model-Based Probabilistic Reasoning for Electronics Troubleshooting

Richard R. Cantone, Frank J. Pipitone, W. Brent Lander, Michael P. Marrone

*Proceedings of the Eighth International Joint Conference on Artificial Intelligence*, pages 207-210.

## 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.