Northrop Grumman Systems Corporation

Patrick James Talbot

Dennis Regan Ellis

Keith Eugene Mathias

Jeffrey Allen McCartney

Mark Robert Nixon

J Ryan Sanders

Systems and methods are provided for assisted decision-making. A knowledge base comprises a plurality of executable stories. A given story comprises a belief network containing at least one associated hypothesis and evidence associated with the at least one hypothesis. The system further comprises at least one decision algorithm having respective associated input formats. A translation interface is operative to convert a given story from the knowledge base into a decision network having a format associated with the at least one decision algorithm.

Knowledge base comprising executable stories

Systems and methods are provided for determining unknown unknowns for at least one assisted decision making system. An assisted decision making system has an associated set of at least one story of interest. The system further comprises a plurality of inferencing algorithms. A given inferencing algorithm is operative to generate story fragments from data stored within an associated knowledge base. A first arbitrator is associated with the first assisted decision making system. The arbitrator accepts a story fragment from the plurality of inferencing algorithms if the story fragment is sufficiently related to a story of interest from the set of at least one story of interest.

System and method for the automated discovery of unknown unknowns

Systems and methods are provided for generating a decision network from text. An information extraction component extracts a quantum of evidence and an associated confidence value from a given text segment. An evidence classifier associates each quantum of evidence with one of a plurality of hypotheses. A fusion engine builds a decision network from the plurality of hypotheses, an associated base structure, the extracted quanta of evidence, and the confidence values.

Systems and methods for generating a decision network from text

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.

Interactive course of action analysis tool using case based injected genetic algorithm

Systems and methods are provided for constructing and evaluating a story of interest. The system includes a plurality of decision algorithms. Each decision algorithm is operative to quantify at least one category of uncertainty associated with the story of interest as a set of at least one uncertainty parameter. An uncertainty management component is operative to reconcile the sets of uncertainty parameters from the plurality of decision algorithms as to produce a global uncertainty parameter for each of the plurality of uncertainty categories for the story of interest.

Uncertainty management in a decision-making system

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.

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Colorado Springs, CO, United States of America

Patrick James Talbot