The role of parallel processing in heuristic search is examined by means of an example (cryptarithmetic addition). A problem solver is constructed that combines the metaphors of constraint propagation and hypothesize-and-test. The system is capable of working on many incompatible hypotheses at one time. Furthermore, it is capable of allocating different amounts of processing power to running activities and and changing these allocations as computation proceeds. It is empirically found that the parallel algorithm is, on the average, more efficient than a corresponding sequential one. Implications of this for problem solving in general are discussed.
