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.

This book constitutes the refereed proceedings of the 12th IFIP TC 12 International Conference on Intelligent Information Processing, IIP 2022, held in Qingdao, China, in July 2022. The 37 full papers and 6 short papers presented were carefully reviewed and selected from 57 submissions. They are organized in topical sections on Machine Learning, Data Mining, Multiagent Systems, Social Computing, Blockchain Technology, Game Theory and Emotion, Pattern Recognition, Image Processing and Applications.

This book constitutes the refereed proceedings of the 12th IFIP TC 12 International Conference on Intelligent Information Processing, IIP 2022, held in Qingdao, China, in July 2022.   The 37 full papers and 6 short papers presented were carefully reviewed and selected from 57 submissions. They are organized in topical sections on Machine Learning, Data Mining, Multiagent Systems, Social Computing, Blockchain Technology, Game Theory and Emotion, Pattern Recognition, Image Processing and Applications.

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.

This volume contains the proceedings of the 6th Symposium on Abstraction, Reformulation and Approximation (SARA 2005). The symposium was held at Airth Castle, Scotland, UK, from July 26th to 29th, 2005, just prior to the IJCAI 2005 conference in Edinburgh. Previous SARA symposia took place at JacksonHole in Wyoming, USA (1994), Ville d'Estrel in Qubec, Canada (1995), Asilomar in California, USA (1998), Horseshoe Bay, Texas, USA (2000), and Kananaskis, Alberta, Canada (2002). This was then the ?rst time that the s- posium was held in Europe. Continuing the tradition started with SARA 2000, the proceedings have been published in the LNAI series of Springer. Abstractions, reformulations and approximations (AR&A) have found app- cationsin avarietyofdisciplines andproblems, including constraintsatisfaction, design, diagnosis, machine learning, planning, qualitative reasoning, scheduling, resource allocation and theorem proving, but are also deeply rooted in philo- phy and cognitive science. The papers in this volume capture a cross-section of the various facets of the ?eld and of its applications. One of the primary uses of AR&A is oriented to overcome computational intractability. AR&A techniques, however, have also proved useful for knowledge acquisition, explanation and other applications, as papers in this volume also illustrate.