Much of AI research is about problem-solving strategies, and
several techniques have been crystalized. One such technique is
constraint satisfaction or reasoning based on relations.
Constraint-based reasoning is used to solve a wide field of
problems, and recently constraint techniques have been incorporated
into logic programming languages, yielding a whole new field of
research and application: constraint logic programming. Constraint
satisfaction techniques have become part of almost all introductory
books on AI. This monograph is about constraint satisfaction. It
differs from others in that it presents all approaches under a
common, generalizing view: dynamic constraints. This new way of
viewing constraints provides new insights about the different
approaches, and forms a very practical basis for teaching
constraint-based reasoning. A uniform view of the constraint world
is also a good basis for constraint research. This text is not
intended to be a self-contained textbook on constraint-based
reasoning, but rather a coherent text on an interesting view of the
field.
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