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Constraint Handling Using Tournament Selection: Abductive Inference In Partly Deterministic Bayesian Network, Severino F. Galan, Ole J. Mengshoel
Constraint Handling Using Tournament Selection: Abductive Inference In Partly Deterministic Bayesian Network, Severino F. Galan, Ole J. Mengshoel
Ole J Mengshoel
Constraints occur in many application areas of interest to evolutionary computation. The area considered here is Bayesian networks (BNs), which is a probability-based method for representing and reasoning with uncertain knowledge. This work deals with constraints in BNs and investigates how tournament selection can be adapted to better process such constraints in the context of abductive inference. Abductive inference in BNs consists of finding the most probable explanation given some evidence. Since exact abductive inference is NP-hard, several approximate approaches to this inference task have been developed. One of them applies evolutionary techniques in order to find optimal or close-to-optimal …
Methods For Probabilistic Fault Diagnosis: An Electrical Power System Case Study, Brian Ricks, Ole J. Mengshoel
Methods For Probabilistic Fault Diagnosis: An Electrical Power System Case Study, Brian Ricks, Ole J. Mengshoel
Ole J Mengshoel
Health management systems that more accurately and quickly diagnose faults that may occur in different technical systems on-board a vehicle will play a key role in the success of future NASA missions. We discuss in this paper the diagnosis of abrupt continuous (or parametric) faults within the context of probabilistic graphical models, more specifically Bayesian networks that are compiled to arithmetic circuits. This paper extends our previous research, within the same probabilistic setting, on diagnosis of abrupt discrete faults. Our approach and diagnostic algorithm ProDiagnose are domain-independent; however we use an electrical power system testbed called ADAPT as a case …