15. A computer program product as set forth in claim 13 , further comprising instruction means for causing a computer to perform operations of: identifying a set of variables in the Dempster-Shafer Reasoning System, where each variable in the set has at least one input basic probability assignment (bpa); identifying at least one output variable, the output variable having output bpa corresponding to the input bpa; defining a gradient of an objective function in terms of a discounting weight vector, an output bpa, and a partial derivative of the output bpa with respect to the discounting weight vector; calculating an output bpa for each variable with its input bpa, without discounting, and storing the result; recalculating the output bpa for each variable without applying its input bpa, and storing the result; using the results obtained in the acts of calculating and recalculating to redefine the gradient according to the act of defining the gradient; updating the discounting weight vector; and repeating the acts of defining through updating until convergence of the discounting weight vector, and where upon convergence, the weights reflected in the discounting weight vector are optimal weights for use in the act of optimizing a weight to assign to each of the inputs.
15. A computer program product as set forth in claim 13 , further comprising instruction means for causing a computer to perform operations of: identifying a set of variables in the Dempster-Shafer Reasoning System, where each variable in the set has at least one input basic probability assignment (bpa); identifying at least one output variable, the output variable having output bpa corresponding to the input bpa; defining a gradient of an objective function in terms of a discounting weight vector, an output bpa, and a partial derivative of the output bpa with respect to the discounting weight vector; calculating an output bpa for each variable with its input bpa, without discounting, and storing the result; recalculating the output bpa for each variable without applying its input bpa, and storing the result; using the results obtained in the acts of calculating and recalculating to redefine the gradient according to the act of defining the gradient; updating the discounting weight vector; and repeating the acts of defining through updating until convergence of the discounting weight vector, and where upon convergence, the weights reflected in the discounting weight vector are optimal weights for use in the act of optimizing a weight to assign to each of the inputs. 16. A computer program product as set forth in claim 15 , wherein in defining a gradient of an objective function in terms of a discounting weight vector, the objective function is based on the combined bpa on a variable XεU, with variable frame Θ X , and is defined as: J ⁡ ( α ) = ∑ k ⁢ ⁢ ∑ l ⁢ ⁢ [ ∑ j ⁢ ⁢ ( P kl ⁡ ( a j ) - δ kj ) 2 + λ * m ⁡ ( ϕ ) ] , where P kl is an (un-normalized) pignistic probability given by: P kl ⁡ ( a j ) = ∑ C ⁢ ⊆ ⁢ Θ X , a j ∈ ⁢ C ⁢ ⁢ m ⁡ ( C )  C  , ∀ a j ∈ ⁢ Θ X , where m(·) is an un-normalized bpa on the variable frame of Θ X , and δ kj = { 1 if k = j 0 if k ≠ j ⁢ ⁢ represents ⁢ ⁢ a ⁢ ⁢ given ⁢ ⁢ truth ⁢ ⁢ bpa ⁢ ⁢ on ⁢ ⁢ a ⁢ ⁢ same ⁢ ⁢ variable ⁢ ⁢ frame , where φ represents an empty set, m(φ) represents mass on conflict as all bpa's refer to un-normalized bpa's, and ∥ ∥ denotes an operator.