Abstract: An automated diagnostic system uses Bayesian networks to diagnose a system. Knowledge acquisition is performed in preparation to diagnose the system. An issue to diagnose is identified. Causes of the issue are identified. Subcauses of the causes are identified. Diagnostic steps are identified. Diagnostic steps are matched to causes and subcauses. Probabilities for the causes and the subcauses identified are estimated. Probabilities for actions and questions set are estimated. Costs for actions and questions are estimated.

Abstract: An automated troubleshooter uses Bayesian networks to troubleshoot a system. Knowledge acquisition is performed in preparation to troubleshoot the system. An issue to troubleshoot is identified. Causes of the issue are identified. Subcauses of the causes are identified. Troubleshooting steps are identified. Troubleshooting steps are matched to causes and subcauses. Probabilities for the causes and the subcauses identified are estimated. Probabilities for actions and questions set are estimated. Costs for actions and questions are estimated.

Abstract: A Bayesian network includes a set of nodes representing discrete-valued variables. A plurality of arcs connect nodes from the set of nodes. The arcs represent the causal dependencies between the nodes. A prior marginal probability value is associated with each leaf node. The prior marginal probability values are calculated by first estimating conditional probabilities for nodes. For example, for each node with parent nodes, knowledge acquisition questions are developed which when answered will indicate the conditional probability of each parent node of the node. The questions and answer assume a single occurrence of a fault (the single-fault assumption). That is, it is assumed that one and only one variable represented by the plurality of parent nodes will be in its positive state (i.e., the fault will occur). Thus a sum of the conditional probabilities for all variables represented by all parent nodes for any particular node will always be equal to one.

Abstract: An automated diagnostic system uses Bayesian networks to diagnose a system. Knowledge acquisition is performed in preparation to diagnose the system. An issue to diagnose is identified. Causes of the issue are identified. Subcauses of the causes are identified. Diagnostic steps are identified. Diagnostic steps are matched to causes and subcauses. Probabilities for the causes and the subcauses identified are estimated. Probabilities for actions and questions set are estimated. Costs for actions and questions are estimated.