We consider computer assisted troubleshooting of automotive vehicles, where the objective is to repair the vehicle at as low expected cost as possible.

The work has three main contributions: a troubleshooting method that applies to troubleshooting in real environments, the discussion on practical issues in modeling for troubleshooting, and the efficient probability computations.

The work is based on a case study of an auxiliary braking system of a modern truck.

We apply a decision theoretic approach, consisting of a planner and a diagnoser.

Two main challenges in troubleshooting automotive vehicles are the need for disassembling the vehicle during troubleshooting to access parts to repair, and the difficulty to verify that the vehicle is fault free. These facts lead to that probabilities for faults and for future observations must be computed for a system that has been subject to external interventions that cause changes the dependency structure. The probability computations are further complicated due to the mixture of instantaneous and non-instantaneous dependencies.

To compute the probabilities, we develop a method based on an algorithm, updateBN, that updates a static BN to account for the external interventions.