The CDP: A unifying formulation for heuristic search, dynamic programming, and branch-and-bound

This paper presents the composite decision process (CDP), a general model for discrete optimization problems. Using certain relationships between formal grammars, AND/OR graphs, and game trees, it is shown that a large number of search problems in artificial intelligence can be formulated in terms of this model. Two general classes of algorithms are discussed, and it is shown that most of the existing search algorithms to solve problems represented by this model fall into one of two categories. This approach to formulating and solving discrete optimization problems makes it possible to view several search procedures in a unified manner and clarifies the relationships among them. The approach also aids in synthesizing new variations and generalizations of existing search procedures.

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