Shultz, T. R. (2001). Constraint satisfaction models. In N. J. Smelser & P. B. Baltes (Eds.), International Encyclopedia of the Social and Behavioral Sciences (Vol. 4, pp. 2648-2651). Oxford: Pergamon.



Constraint-satisfaction problems are characterized by definition and example. The essential algorithm and underlying mathematics for implementing constraint satisfaction in artificial neural networks are described, along with notable variations. Work on symbolic constraint satisfaction in Artificial Intelligence is briefly discussed. Application of constraint-satisfaction neural networks to a variety of psychological phenomena are mentioned, including content-addressable memory, schema completion, analogy mapping, explanation, decision making, impression formation, attitude change, cognitive dissonance, visual object recognition, word pronunciation, and neuropsychological deficits. Analogies with physical concepts, such as energy, annealing, and temperature are provided. Possible future developments in constraint-satisfaction research are identified.


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