TY - JOUR AU - Stern, Roni AU - Juba, Brendan PY - 2019/07/17 Y2 - 2024/03/28 TI - Safe Partial Diagnosis from Normal Observations JF - Proceedings of the AAAI Conference on Artificial Intelligence JA - AAAI VL - 33 IS - 01 SE - AAAI Technical Track: Knowledge Representation and Reasoning DO - 10.1609/aaai.v33i01.33013084 UR - https://ojs.aaai.org/index.php/AAAI/article/view/4167 SP - 3084-3091 AB - <p>Model-based diagnosis (MBD) is difficult to use in practice because it requires a model of the diagnosed system, which is often very hard to obtain. We explore theoretically how observing the system when it is in a normal state can provide information about the system that is sufficient to learn a partial system model that allows automated diagnosis. We analyze the number of observations needed to learn a model capable of finding faulty components in most cases. Then, we explore how knowing the system topology can help us to learn a useful model from the normal observations for settings in which many of the internal system variables cannot be observed. Unlike other data-driven methods, our learned model is safe, in the sense that subsystems identified as faulty are guaranteed to truly be faulty.</p> ER -