In an inference engine a conditional dependency of variables is characterized in terms of second order uncertainty to aid in improving decision making speed and precision. Mean and distribution of evidence states are utilized to provide first order uncertainties for each of a plurality of states. Higher order statistics such as standard deviation and variance for the states are calculated in order to define second order uncertainties. A covariance layer of the inference engine receives variance information from parent nodes for calculating the states of a child node. Second order uncertainty expresses conditional dependency of the parameters to which the child node responds. The method and apparatus are generalized to apply the propagation of second order uncertainty through inference engines such as Bayesian Networks, Influence Diagrams and Probabilistic Relational Models, to output control signals.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for encoding conditional dependencies in an inference engine comprising a network and embodying a mathematical model representing a context in which statistical methods are used as an aid in decision making in which uncertainties are propagated between one level and a next level comprising: receiving evidence inputs at a parent node; establishing a selected number of states for each input hypothesis; calculating first order uncertainty of states for a given evidence input; calculating at least one higher order statistic including at least one of standard deviation and variance for the individual states from the evidence inputs, whereby second order uncertainty (SOU) data is provided; propagating the first order uncertainty data and the second order uncertainty data from the parent node to a child node, wherein said second order uncertainty data is used to determine a SOU inference during propagation as an input to the child node; determining covariance of inputs to the child node; propagating the covariance to subsequent nodes. 2. A method according to claim 1 wherein the step of propagating the covariance to subsequent nodes is continued to a utility node and providing at least one utility value. 3. A method according to claim 2 further comprising providing the at least one utility value to a utility table, and allowing selection of one said at least one utility value. 4. A method according to claim 3 comprising the step of providing the network comprising a Bayesian Network. 5. A method according to claim 3 comprising the step of providing the network comprising an Influence Diagram. 6. A method according to claim 3 comprising the step of providing the network comprising a Probabilistic Relational Model. 7. A method according to claim 3 comprising the step of providing the network comprising an output control signal. 8. A method of operating a decision support system comprising: expressing mean values and higher order statistics for encoding conditional dependencies among variables in at least one cell of an associated Conditional Probability Table (CPT); and using conditional dependencies, the mean values and the higher order statistics in the CPT in an inference engine through a network comprising a mathematical model of a decision process, wherein said higher order statistics are used to determine a SOU inference. 9. The method according to claim 8 wherein the method of operating a decision support system further comprises calculating mean values and higher order statistic including standard deviations for each state of a plurality of states of a potential. 10. The method according to claim 9 wherein the expressing step comprises storing mean values and deviations from the mean in at least one cell of a utility table. 11. The method according to claim 8 comprising calculating higher order statistics for each individual state in a plurality of the states from evidence inputs, whereby second order uncertainty data is provided for the individual states, and propagating the second order uncertainty data from a parent node to a child node. 12. The method according to claim 10 comprising expressing each deviation from the mean value of an individual state in terms of a positive deviation and a negative deviation. 13. A method according to claim 8 comprising the step of providing the network in the form of a Bayesian Network. 14. A method according to claim 10 including the step of providing the network in the form of an Influence Diagram. 15. A method according to claim 10 comprising the step of providing the network in the form of a Probabilistic Relational Model. 16. A method according to claim 10 comprising the step of providing the network comprising an output control signal. 17. A non-transitory machine-readable medium that provides instructions, which when executed by a processor, causes said processor to perform operations comprising: receiving evidence inputs at a parent node; establishing a selected number of states for each input hypothesis; calculating first order uncertainty of states for a given evidence input; calculating at least one higher order statistic including at least one of standard deviation and variance for the individual states from the evidence inputs, whereby second order uncertainty (SOU) data is provided; propagating the first order uncertainty data and the second order uncertainty data from the parent node to a child node, wherein said second order uncertainty data is used to determine a SOU inference during propagation as an input to the child node; determining covariance of inputs to the child node; propagating the covariance to subsequent nodes. 18. A non-transitory machine-readable medium according to claim 17 further causing said processor to perform operations of propagating both mean values and covariance of potentials through the inference engine, storing CPTs and Utility Tables, and calculating mean values and higher order statistics including standard deviations of evidence obtained from interfaces. 19. A non-transitory machine-readable medium that provides instructions, which when executed by a processor, causes said processor to perform operations comprising: expressing mean values and higher order statistics for encoding conditional dependencies among variables in at least one cell of an associated Conditional Probability Table (CPT); and using conditional dependencies, the mean values and the higher order statistics in the CPT in an inference engine through a network comprising a mathematical model of a decision process, wherein said higher order statistics are used to determine a SOU inference. 20. The non-transitory machine readable medium according to claim 19 further comprising calculating mean values and higher order statistics including standard deviations for each of a plurality of states of a potential. 21. An inference system embodying a mathematical model representing a context, the inference system comprising: an interface layer, a covariance layer and an inference engine: the inference engine comprising a processor calculating both mean values and second order uncertainty (SOU) data for individual states in a plurality of evidence input states and covariance of potentials at nodes in the inference engine, the processor comprising storage for Conditional Probability Tables; the inference engine further comprising parent and child nodes, child nodes receiving the calculated mean and the calculated second order uncertainty data for each state of a plurality of states propagating from at least one parent node, wherein said SOU data is used to determine a SOU inference; nodes being coupled to propagate in accordance with the mathematical model to a utility node. 22. An inference system according to claim 21 further comprising a circuit resolving each range within a potential into a selected number of first order uncertainty states and wherein said processor is programmed to calculate a mean value for each state and to calculate higher order statistics including at least one of standard deviation and variance for each state of the plurality of states, whereby a second order uncertainty is provided to determine covariance of inputs to a node. 23. A method of operating a decision support system comprising: expressing mean values and deviations from the mean for conditional dependencies in at least one cell of an associated Utility Table; and using conditional dependencies, the mean values and the deviations from the mean in the Utility Table in an inference engine through a network comprising a mathematical model of a decision process, wherein said deviations from the mean in the Utility Table are used to determine a range of expected utility.