Abstract: An authoring tool assists an author in building an automated troubleshooter for a product. The authoring tool includes a cause editor interface, an action editor interface and a question editor interface. The cause editor interface allows an author to place, in a cause data structure, information pertaining to causes of malfunction of the product. The action editor interface allows an author to place, in an action data structure, information pertaining to actions that can be taken to correct malfunction of the product. The question editor interface allows an author to place, in a question data structure, information pertaining to questions that can be asked a user of the product to help identify causes of malfunction of the product.

Abstract: A decision support system supports investment decisions. The decision support system includes a decision support engine and a Bayesian network. The Bayesian network is traversed by the decision support engine. The Bayesian network includes an investment decision node, potential investment nodes, and information nodes. The investment decision node identifies an investment decision. The potential investment nodes identify potential investments. The information nodes identify information to be obtained. The information to be obtained is matched to potential investments. Reliability of the information is estimated, for example, with the help of domain experts.

Abstract: An authoring tool assists an author in building an automated diagnostic system. The authoring tool includes a cause editor interface, an action editor interface and a question editor interface. The cause editor interface allows an author to place, in a cause data structure, information pertaining to causes. The action editor interface allows an author to place, in an action data structure, information pertaining to actions that can be taken in response to the causes. The question editor interface allows an author to place, in a question data structure, information pertaining to questions that can be asked a user of the product to help identify causes.

Abstract: Model selection is performed. First information is obtained from a user about a presenting issue. The first information is used within a supermodel to identify an underlying issue and an associated sub model for providing a solution to the underlying issue. A Bayesian network structure is used to identify the underlying issue and the associated sub model. The sub model obtains additional information about the underlying issue from the user. The sub model uses the additional information to identify a solution to the underlying issue.

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: A probabilistic diagnostic system is validated. A diagnostic sequence is generated from a diagnostic model. The diagnostic sequence is evaluated to determine whether the diagnostic sequence provides an acceptable resolution to a problem. This is repeated for additional diagnostic sequences from the diagnostic model. It is determined whether a predetermined number of diagnostic sequences provide an acceptable resolution. When it is determined that the predetermined number of diagnostic sequences provide an acceptable resolution, the diagnostic model is accepted.

Abstract: Model selection is performed. First information is obtained from a user about a presenting issue. The first information is used within a supermodel to identify an underlying issue and an associated sub model for providing a solution to the underlying issue. A Bayesian network structure is used to identify the underlying issue and the associated sub model. The sub model obtains additional information about the underlying issue from the user. The sub model uses the additional information to identify a solution to the underlying issue.

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: A decision support system supports investment decisions. The decision support system includes a decision support engine and a Bayesian network. The Bayesian network is traversed by the decision support engine. The Bayesian network includes an investment decision node, potential investment nodes, and information nodes. The investment decision node identifies an investment decision. The potential investment nodes identify potential investments. The information nodes identify information to be obtained. The information to be obtained is matched to potential investments. Reliability of the information is estimated, for example, with the help of domain experts.

Abstract: Methods and structure for estimating computational resource complexity for a Bayesian belief network (“BBN”) model for problem diagnosis and resolution. Bayesian belief networks may be bounded with respect to application to resolution of particular problem. Such bounded BBNs are found to consume memory resources in accordance with a mathematical model of polynomial complexity or less. Applying this model to estimate the computational memory resources required for computation of the BBN model permits effective management of distributing BBN computations over a plurality of servers. Such distribution of BBN computations enables improved responsiveness to servicing multiple clients requesting BBN applications to multiple problem resolutions.

Abstract: An authoring tool assists an author in building an automated diagnostic system. The authoring tool includes a cause editor interface, an action editor interface and a question editor interface. The cause editor interface allows an author to place, in a cause data structure, information pertaining to causes. The action editor interface allows an author to place, in an action data structure, information pertaining to actions that can be taken in response to the causes. The question editor interface allows an author to place, in a question data structure, information pertaining to questions that can be asked a user of the product to help identify causes.

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.