Abstract
This chapter reviews that constraint logic programming (CLP) is the merger of two declarative paradigms: constraint solving and logic programming. As both constraint solving and logic programs are based on mathematical relations the merger is natural and convenient. CLP encourages experimentation and fast algorithm development by narrowing the gap between the logic and the solving algorithms. This is because CLP can express both conceptual and design models and it can also express mappings from conceptual to design models. The “conceptual” model of a problem means the precise formulation of the problem in logic, and the “design” models of the problem means its algorithmic formulation, which maps to a sequence of steps for solving it. A single problem may have different conceptual models, and different design models. The chapter also reviews the first important characteristic of constraint logic programs—that is, they allow succinct, natural conceptual modeling of satisfaction and optimization problems.
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