Abstraction in Artificial Intelligence and Complex Systems azw3 网盘 高速 下载地址大全 免费

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）

1 Introduction........................................ 1 1.1 Summary...................................... 9 2 Abstraction in Different Disciplines....................... 11 2.1 Philosophy..................................... 11 2.2 Natural Language................................ 18 2.3 Mathematics ................................... 20 2.4 Computer Science................................ 23 2.5 Art(Mostly Peinture) ............................. 28 2.6 Cognition...................................... 31 2.7 Vision........................................ 37 2.8 Summary...................................... 47 3 Abstraction in Artificial Intelligence...................... 49 3.1 Theoretical Approaches............................ 49 3.2 Abstraction in Planning............................ 55 3.3 Abstraction in Constraint Satisfaction Problems . . . . . . . . . . . 59 3.4 Abstraction in Knowledge Representation............... 60 3.5 Abstraction in Agent-Based Modeling.................. 62 3.6 Summary...................................... 63 4 Definitions of Abstraction.............................. 65 4.1 Giunchiglia and Walsh’ Theory...................... 66 4.2 Abstraction in Philosophy.......................... 70 4.2.1 Wright and Hale’s Abstraction Principles . . . . . . . . . 70 4.2.2 Floridi’s Levels of Abstraction................. 71 4.3 Abstraction in Computer Science..................... 77 4.4 Abstraction in Databases........................... 79 4.4.1 Miles Smith and Smith’s Approach ............. 79 4.4.2 Goldstein and Storey’s Approach............... 83 4.4.3 Cross’ Approach........................... 84 4.5 Granularity .................................... 87 4.5.1 Hobbs’ Approach.......................... 87 4.5.2 Imielinski’s Approach....................... 89 4.5.3 Fuzzy Sets............................... 91 4.5.4 Rough Sets .............................. 92 4.6 Syntactic Theories of Abstraction..................... 94 4.6.1 Plaisted’s Theory of Abstraction................ 94 4.6.2 Tenenberg’s Theory ........................ 96 4.6.3 DeSaeger and Shimojima’s Theory............. 99 4.7 Semantic Theories of Abstraction..................... 103 4.7.1 NayakandLevy’s Theory.................... 103 4.7.2 Ghidini and Giunchiglia’s Theory............... 106 4.8 Reformulation .................................. 111 4.8.1 Lowry’s Theory........................... 112 4.8.2 Choueiryetal.’s Approach ................... 113 4.8.3 Subramanian’s Approach..................... 114 4.9 Summary...................................... 115 5 Boundaries of Abstraction ............................. 117 5.1 Characteristic Aspects of Abstraction .................. 118 5.1.1 Abstraction as Information Reduction. . . . . . . . . . . . 118 5.1.2 Abstraction as an Intensional Property ........... 120 5.1.3 Abstraction as a Relative Notion ............... 123 5.1.4 Abstraction as a Process ..................... 125 5.1.5 Abstraction as Information Hiding .............. 129 5.2 Boundaries of Abstraction.......................... 130 5.2.1 Abstraction and Generalization/Categorization . . . . . . 130 5.2.2 Abstraction, Approximation, and Reformulation. . . . . 135 5.3 Summary...................... ................ 139 6 The KRA Model.................... ................ 141 6.1 Query Environment, Description Frame,and ConfigurationSpace........................... 142 6.2 Query Environment............................... 152 6.3 Data Generation................................. 161 6.4 The KRA Model of Abstraction ..................... 163 6.5 Summary...................................... 175 7 Abstraction Operators and Design Patterns................. 179 7.1 A Classification of Abstraction Operators............... 179 7.2 Hiding Operators ................................ 180 7.2.1 Hiding Element Operators.................... 181 7.2.2 Hiding Value Operators...................... 182 7.2.3 Hiding Argument Operators................... 184 7.3 Building Equivalence Classes Operators ................ 185 7.3.1 Operators Building Equivalence Classes of Elements.............................. 186 7.3.2 Operators Building Equivalence Classes of Values . . . 189 7.3.3 Operators Building Equivalence Classes of Arguments............................. 190 7.4 Hierarchy Generating Operators...................... 190 7.4.1 Operator that Builds a Hierarchy of Types:ωhiertype.......................... 191 7.4.2 Operator that Builds a Hierarchy of Attribute Values:ωhierattrval .......................... 192 7.5 Composition Operators............................ 193 7.5.1 Operator that Builds a Collective Object: ωcoll . . . . . . 193 7.5.2 Operator that Aggregates Objects/Types: ωaggr. . . . . . 194 7.5.3 Operator that Builds up a Group of Objects: ωgroup.......................... 195 7.5.4 Operator that Constructs a New Description Element: ωconstr ........................... 196 7.6 Approximation Operators........................... 197 7.6.1 Replacement Operator: ρrepl................... 199 7.6.2 Identification Operator ...................... 199 7.7 Reformulation .................................. 201 7.8 Overview of Operators ............................ 202 7.9 Abstraction Processes............................. 203 7.10 Applying Abstraction: the Method .................... 204 7.10.1 Abstracting a P-Set with a Method.............. 204 7.11 Abstraction Processes and Query Environment. . . . . . . . . . . . 213 7.12 From Abstraction Operators to Abstraction Patterns . . . . . . . . 216 7.12.1 Design Patterns ........................... 217 7.12.2 Use and Motivation for Design Patterns .......... 218 7.12.3 Abstraction Patterns ........................ 218 7.12.4 Abstraction Pattern:Hiding................... 220 7.13 Summary...................................... 220 8 Properties of the KRA Model.......................... 223 8.1 Abstraction, Approximation, and Reformulation . . . . . . . . . . . 223 8.2 Abstraction and Information......................... 227 8.3 Approximation and Information...................... 230 8.4 Reformulation and Information ...................... 232 8.5 Query Environment and Abstraction Operators. . . . . . . . . . . . 232 8.6 Abstraction versus Concretion....................... 234 8.7 Inconsistency Problem............................. 239 8.8 KRA’s Unification Power.......................... 244 8.8.1 Theories Defined at the Perception (Observation)Level ........................ 244 8.8.2 Semantic Theories of Abstraction............... 255 8.8.3 Syntactic Theories of Abstraction............... 264 8.9 KRA and Other Models of Abstraction ................ 266 8.10 Special Cases................................... 268 8.11 Summary...................................... 270 9. Abstraction in Machine Learning........................ 273 9.1 A Brief Introduction to Machine Learning............... 275 9.2 Abstraction in Learning from Examples or Observations. . . . . 277 9.2.1 Feature Selection .......................... 278 9.2.2 Instance Selection.......................... 283 9.2.3 Feature Discretization....................... 285 9.2.4 Constructive Induction ...................... 286 9.3 Abstraction in Reinforcement Learning................. 294 9.3.1 State Space Abstraction in Reinforcement Learning................................ 297 9.3.2 Function Approximation in Reinforcement Learning................................ 299 9.3.3 Task Decomposition and Hierarchical Reinforcement Learning ..................... 300 9.3.4 Temporal Abstraction in Reinforcement Learning. . . . 302 9.4 Abstraction Operators in Machine Learning.............. 303 9.4.1 Modeling Propositional Concept Learning in the KRA Model ......................... 303 9.4.2 Answering a Query Q in Propositional Concept Learning.......................... 305 9.4.3 Feature Selection in Propositional Learning . . . . . . . . 307 9.4.4 Modeling Relational Concept Learning in the KRA Model ......................... 309 9.4.5 Modeling Reinforcement Learning ιn the KRA Model ......................... 320 9.5 Summary...................................... 326 10 Simplicity,Complex Systems,and Abstraction............... 329 10.1 Complex Systems................................ 329 10.1.1 Abstraction in Complex Systems ............... 334 10.2 Complexity and Simplicity ......................... 338 10.3 Complexity Measures............................. 341 10.3.1 Kolmogorov Complexity..................... 342 10.3.2 Normalized Complexity...................... 346 10.3.3 Logical Depth ............................ 347 10.3.4 Thermodynamic Depth ...................... 349 10.3.5 Gamma Function (Simple Complexity). . . . . . . . . . . 349 10.3.6 Sophistication............................. 350 10.3.7 Effective Complexity ....................... 351 10.3.8 Predictive Information Rate................... 352 10.3.9 Self-Dissimilarity.......................... 353 10.4 Abstraction and Complexity......................... 354 10.4.1 Turing Machine-Based Complexity Measures . . . . . . 354 10.4.2 Stochastic Measures of Complexity ............. 357 Summary...................................... 361 11 Case Studies and Applications........................... 363 11.1 Model-Based Diagnosis............................ 363 11.1.1 An Example: The Fragment of an Hydraulic System...................... 367 11.2 Cartographic Generalization......................... 371 11.2.1 Operator Learning for Cartographic Generalization. . . 378 11.3 Hierarchical Hidden MarkovModels................... 384 11.4 Summary...................................... 387 12 Discussion ......................................... 389 12.1 Analogy ...................................... 389 12.2 Computational Complexity ......................... 393 12.2.1 Complexity Reduction in Search ............... 394 12.2.2 Complexity Reduction in CSPs ................ 395 12.2.3 Complexity Reduction in Machine Learning . . . . . . . 397 12.3 Extensions of the KRAModel ...................... 402 12.3.1 The G-KRA Model........................ 402 12.3.2 Hendriks’ Model .......................... 403 12.4 Summary...................................... 404 13 Conclusion......................................... 407 13.1 Ubiquity of Abstraction............................ 407 13.2 Difficulty o f a Formal Definition..................... 408 13.3 The Need for an Operational Theory of Abstraction . . . . . . . . 408 13.4 Perspectives of Abstraction in AI..................... 410 Appendix A: Concrete Art Manifesto......................... 413 Appendix B: Cartographic Results for Roads................... 415 Appendix C: Relational Algebra ............................ 417

Lorenza Saitta is Full Professor of Computer Science at the University of Torino (Italy). She started her research activity in Pattern Recognition, moving soon to AI, specifically in the area of Fuzzy Logic for Expert Systems. In 1984 she started working in Machine Learning, thus initiating the reaserch in the field in Italy. Her first interests have been in inductive symbolic approaches. In this period, she and her research group developed the systems ML-SMART and RIGEL, which learn first-order logic concept descriptions, and have been applied to real-world industrial problems. Later, she worked on the integration of different learning strategies, involving more complex reasoning schemes, such as deduction and abduction Of this period is the implementation of the system WHY, which exploits examples and a causal model of the domain to acquire and revise first-order logic based diagnostic knowledge. More recently, she became interested in Genetic Algorithms (system REGAL) and in Cognitive Sciences. She is an Action Editor for the Machine Learning Journal, the Convener of the Research Technical Committee of the CEE founded Network Excellence for Machine Learning and Co-Director of the European Science Foundation project on Learning in Humans and Machines. She has also been responsible of, or participated to, several European Research projects. She gave an invited survey on Machine Learning at ECAI-92, and has been Invited Speaker to the Int. Joint Conf. on Artificial Intelligence (IJCAI-93), the European Conf. on Machine Learning (ECML-94), the Int. Workshop in Inductive Logic Programming (ILP-94) and the Int. Workshop on Artificial Intelligence and Cognitive Science (1994) and will give an invited talk at the 3rd Multistrategy Learning Workshop (1996). She authored (or edited) three books and more than 100 papers in journals, books and international conferences. She is (or has been) member of various journals Editorial Board and of many International Conferences Program Committees, as, for instance, the Machine Learning Conference 1992, 1993, 1994, the European Machine Learning Conference 1991, 1993, 1994, and ECAI-92. She has been Co-Chairperson of the Int. Conf. on Information Processing and Management of Uncertainty (IPMU-88) and of the Int. Symposium of Methodologies for Intelligent Systems. Jean-daniel Zucker My research focus is AI, Machine Learning, Multi-Scale Agent-based modelling of Complex Systems from Omics data integration to Environmental Decision system. I am a Former Engineer (Sup’Aéro,1985). In 1996 I got my Ph.D. in Machine Learning from Paris 6 Univ. where I became an associate professor. In 2002 I became Full Prof. at Paris 13 University. In 2008 I became a Senior Researcher at IRD. I am the director of the UMMISCO Lab. on Math. and Comput.Modeling of Complex Systems since 2014.

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