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Constraint-based spatial reasoning problems frequently arise in the area of military mission planning. In this domain, mission planners employ complex criteria, which may include numeric and optimization constraints in addition to logical constraints and rules, to develop engineering construction and resource deployment plans. Automated planning aid systems for the military must have the capability to represent the various types of constraints used in human decision-making and must be able to provide efficient and optimal or near optimal solutions to the resulting constraint satisfaction problems. This paper describes a methodology for transforming constraint satisfaction problems into nonlinear optimization problems and for solving the resulting optimization problems using a hybrid neural network/genetic algorithm procedure. The method is applied to illustrative problems which employ different types of constraints for determination of future construction sites. The results of the experiments demonstrate the potential of this methodology for finding feasible and optimal solutions to nonlinear optimization problems. © 1994, Elsevier B.V.

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