Abstract: Systems and methods are provided for generating and evaluating a plurality of courses of action (COAs) for a problem of interest having an associated set of problem characteristics. A case based reasoner generates an initial set of courses of action according to the associated problem characteristics. Each course of action includes a plurality of COA packages and a set of associated fitness parameters. A genetic algorithm refines the initial set of courses of action according to a set of global influence values to produce at least one optimized course of action. A user interface allows a user to adjust at least one value from the set of global influence values and the set of fitness parameters associated with each optimized course of action.

Abstract: Systems and methods are provided for fusing new evidence and displaying node parameters of a decision network. The decision network can be a belief network, such as a Dempster-Shafer belief network. The Dempster-Shafer belief network includes node parameters that conform to the Dempster-Shafer combination rule, which is based on an evidential interval: the sum of a belief value, a disbelief value and an unknown value is equal to one. A user override is provided to allow a user to override node parameters associated with a hypothesis or outcome and the decision network self-adjusts the appropriate link values or learned to instantiate the override. The back-propagation algorithm is used to adjust the links.

Abstract: Systems and methods are provided for fusing new evidence and displaying node parameters of a decision network. The decision network can be a belief network, such as a Dempster-Shafer belief network. The Dempster-Shafer belief network includes node parameters that conform to the Dempster-Shafer combination rule, which is based on an evidential interval: the sum of a belief value, a disbelief value and an unknown value is equal to one. A user override is provided to allow a user to override node parameters associated with a hypothesis or outcome and the decision network self-adjusts the appropriate link values or learned to instantiate the override. The back-propagation algorithm is used to adjust the links.