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By Edward Tsang

This textbook examines the constraint delight challenge (CSP), that is a common challenge in AI purposes. It goals to supply a complete creation to the CSP, protecting theoretical, functional and implementation concerns. The ebook discusses formal definitions, CSP fixing algorithms and implementation of a few of the algorithms on PROLOG. the most goal of this booklet is to spot the homes of CSPs and introduce algorithms for tackling CSP. Its major characteristic is the truth that it offers the data in CSP-solving in a single quantity

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G. {machine-203, machine-208, machine-209}. Assigning a value to a variable represents the allocation of a resource to a job. The allocation of resources is normally constrained in many ways. For example, among the M machines available to a job J, only machines P, Q and R have the capacity to cope with job J. Very often, one machine can only process one job at a time. Sometimes, if job J is to use machine Λ/y, then it must also be given certain tools and certain engineers. The task is to allocate to each variable a value such that all the constraints are satisfied.

FC and FFP are discussed in Chapters 5 and 6 respectively. The combination of this search strategy and heuristic has been found to be very effective. CHIP has been applied to a number of problems, and success has been claimed. Some of the reported applications of CHIP include the car-sequencing problem [DiSiVa88b], the spares allocation problem [DVSAG88a], job-shop scheduling, warehouse location, circuit verification (to verify that an implementation of a circuit meets its specifications) [DVSAG88a] and the cut stock problem [DiSiVa88a].

It also has specific characteristics which can be exploited for the development of specialized algorithms. This book is mainly concerned with CSP solving algorithms. 2 Problem Reduction Problem reduction is a class of techniques for transforming a CSP into problems which are hopefully easier to solve or recognizable as insoluble. Although problem reduction alone does not normally produce solutions, it can be extremely useful when used together with search or problem synthesis methods. As we shall see in later chapters, problem reduction plays a very significant role in CSP solving.

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