A completed system is presented that addresses the problem of how do you efficiently execute a knowledge base when in the presence of missing data. Computationally, this is an exponentially expensive operation that without heuristics makes this problem intractable on non-parallel hardware. What is defined is a solution that reduces the complexity of this operation to a more manageable size. This is accomplished through a combination of unique approach that serializes a knowledge base (NTR-42711), a heuristic scoring method (NTR-42712) that characterizes the execution of a rule and a set of heuristics (NTR-42818) that reduces the size of the search space that needs to be explored. Each of these modules is an independent and stand-alone application, but when combined together, they provide a powerful method for generating future scenarios from a knowledge base in the present of missing data.

People who develop software that manipulates code as data or uses advanced symbolic representations as a way to represent data are constantly faced with the problem of how do you determine if a piece of data matches an instance for a given pattern. Typically, this means in each case you have to write a special piece of non- portable code that handles that individual case as a special case. Not only is this a very non-productive and inefficient way to develop software but also it lends itself to the introduction of errors in the code.