Similarity Networks for the Construction of Multiple-Faults Belief Networks
This work addresses the challenge of building diagnostic systems for multiple faults in fields like engineering or medicine, but it appears incremental as it builds directly on existing similarity-network methods.
The paper tackles the problem of constructing belief networks for diagnosing multiple coexisting faults by modifying similarity networks, which were previously limited to single-fault diagnosis, resulting in an extended representation for this more complex scenario.
A similarity network is a tool for constructing belief networks for the diagnosis of a single fault. In this paper, we examine modifications to the similarity-network representation that facilitate the construction of belief networks for the diagnosis of multiple coexisting faults.