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Abstraction is a fundamental mechanism underlying both human and artificial perception, representation of knowledge, reasoning and learning. This mechanism plays a crucial role in many disciplines, notably Computer Programming, Natural and Artificial Vision, Complex Systems, Artificial Intelligence and Machine Learning, Art, and Cognitive Sciences. This book first provides the reader with an overview of the notions of abstraction proposed in various disciplines by comparing both commonalities and differences. After discussing the characterizing properties of abstraction, a formal model, the KRA model, is presented to capture them. This model makes the notion of abstraction easily applicable by means of the introduction of a set of abstraction operators and abstraction patterns, reusable across different domains and applications. It is the impact of abstraction in Artificial Intelligence, Complex Systems and Machine Learning which creates the core of the book. A general framework, based on the KRA model, is presented, and its pragmatic power is illustrated with three case studies: Model-based diagnosis, Cartographic Generalization, and learning Hierarchical Hidden Markov Models. 抽象是人类和人工感知，知识表示，推理和学习的基础机制。这种机制在许多学科中都起着至关重要的作用，特别是计算机编程，自然和人工视觉，复杂系统，人工智能和机器学习，艺术和认知科学。本书首先通过比较通用性和差异性，为读者提供了在各个学科中提出的抽象概念的概述。在讨论了抽象的特性之后，提出了一个正式的模型KRA模型来捕获它们。通过引入一组抽象运算符和抽象模式，该模型可以轻松地应用抽象概念，这些抽象运算符和抽象模式可在不同领域和应用程序之间重用。人工智能，复杂系统和机器学习中的抽象影响是本书的核心。提出了一个基于KRA模型的通用框架，并通过三个案例研究说明了其实用性：基于模型的诊断，制图概括和学习分层隐马尔可夫模型。 （以上为google翻译）