Availability: Artificial intelligence with uncertainty

Artificial intelligence with uncertainty:

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Bibliographic Details
Main Authors:	Li, Deyi (Author), Du, Yi (Author)
Format:	Book
Language:	English
Published:	Boca Raton, Fla. [u.a.] Chapman & Hall/CRC 2008
Subjects:	Artificial intelligence Uncertainty (Information theory) Künstliche Intelligenz Entscheidung bei Unsicherheit Unsicherheit
Links:	http://www.gbv.de/dms/ilmenau/toc/54801549X.PDF http://www.loc.gov/catdir/toc/ecip0712/2007009661.html http://www.loc.gov/catdir/enhancements/fy0806/2007009661-d.html http://www.zentralblatt-math.org/zmath/en/search/?an=1154.68103 http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=020445813&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA
Item Description:	Literaturangaben
Physical Description:	363 S. Ill., graph. Darst., Kt.
ISBN:	1584889985 9781584889984

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Record in the Search Index

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adam_text	IMAGE 1 A R T I F I C I AL IIXITELLIGEIXICE W I TH UIXICERTAIIMTY DEYI LI A ND YI DU TSINGHUA UNIVERSITY BEIJING. CHINA ^ J J C H A P M AN & HALL/CRC M M TAYLOR & FRANCIS GROUP BOCA RATON LONDON NEW YORK CHAPMAN & HALL/CRC IS AN IMPRINT OF THE TAYLOR & FRANCIS GROUP, AN INFORMA BUSINESS IMAGE 2 CONTENTS CHAPTER 1 THE 50-YEAR HISTORY OF ARTIFICIAL INTELLIGENCE 1 1.1 DEPARTURE FROM THE DARTMOUTH SYMPOSIUM 1 1.1.1 COMMUNICATION BETWEEN DIFFERENT DISCIPLINES 1 1.1.2 DEVELOPMENT AND GROWTH 3 1.2 EXPECTED GOALS AS TIME GOES ON 4 1.2.1 TURING TEST 4 1.2.2 MACHINE THEOREM PROOF 5 1.2.3 RIVALRY BETWEEN KASPAROV AND DEEP BLUE 5 1.2.4 THINKING MACHINE 6 1.2.5 ARTIFICIAL LIFE 7 1.3 AI ACHIEVEMENTS IN 50 YEARS 8 1.3.1 PATTERN RECOGNITION 8 1.3.2 KNOWLEDGE ENGINEERING 10 1.3.3 ROBOTICS 11 1.4 MAJOR DEVELOPMENT OF AI IN THE INFORMATION AGE 12 1.4.1 IMPACTS OF AI TECHNOLOGY ON THE WHOLE SOCIETY 12 1.4.2 FROM THE WORLD WIDE WEB TO THE INTELLIGENT GRID 13 1.4.3 FROM DATA TO KNOWLEDGE 14 1.5 THE CROSS TREND BETWEEN AI, BRAIN SCIENCE AND COGNITIVE SCIENCE 15 1.5.1 THE INFIUENCE OF BRAIN SCIENCE ON AI 15 1.5.2 THE INFIUENCE OF COGNITIVE SCIENCE ON AI 17 1.5.3 COMING BREAKTHROUGHS CAUSED BY INTERDISCIPLINES 18 REFERENCES 18 CHAPTER 2 METHODOLOGIES OF AI 21 2.1 SYMBOLISM METHODOLOGY 21 2.1.1 BIRTH AND DEVELOPMENT OF SYMBOLISM 21 2.1.2 PREDICATE CALCULUS AND RESOLUTION PRINCIPLE 24 2.1.3 LOGIC PROGRAMMING LANGUAGE 26 2.1.4 EXPERT SYSTEM 28 2.2 CONNECTIONISM METHODOLOGY 30 2.2.1 BIRTH AND DEVELOPMENT OF CONNECTIONISM 30 2.2.2 STRATEGY AND TECHNICAL CHARACTERISTICS OF CONNECTIONISM 30 2.2.3 HOPFIELD NEURAL NETWORK MODEL 33 2.2.4 BACK-PROPAGATION NEURAL NETWORK MODEL 34 2.3 BEHAVIORISM METHODOLOGY 35 2.3.1 BIRTH AND DEVELOPMENT OF BEHAVIORISM 35 2.3.2 ROBOT CONTROL 36 2.3.3 INTELLIGENT CONTROL 37 IMAGE 3 2.4 REFLECTION ON METHODOLOGIES 38 REFERENCES 39 CHAPTER 3 ON UNCERTAINTIES OF KNOWLEDGE 43 3.1 ON RANDOMNESS 43 3.1.1 THE OBJECTIVITY OF RANDOMNESS 43 3.1.2 THE BEAUTY OF RANDOMNESS 46 3.2 ON FUZZINESS 47 3.2.1 THE OBJECTIVITY OF FUZZINESS 48 3.2.2 THE BEAUTY OF FUZZINESS 49 3.3 UNCERTAINTIES IN NATURAL LANGUAGES 51 3.3.1 LANGUAGES AS THE CARRIER OF HUMAN KNOWLEDGE 51 3.3.2 UNCERTAINTIES IN LANGUAGES 52 3.4 UNCERTAINTIES IN COMMONSENSE KNOWLEDGE 54 3.4.1 COMMON UNDERSTANDING ABOUT COMMON SENSE 54 3.4.2 RELATIVITY OF COMMONSENSE KNOWLEDGE 55 3.5 OTHER UNCERTAINTIES OF KNOWLEDGE 57 3.5.1 INCOMPLETENESS OF KNOWLEDGE 57 3.5.2 INCOORDINATION OF KNOWLEDGE 58 3.5.3 IMPERMANENCE OF KNOWLEDGE 58 REFERENCES 60 CHAPTER 4 MATHEMATICAL FOUNDATION OF AI WITH UNCERTAINTY 61 4.1 PROBABILITY THEORY 61 4.1.1 BAYES THEOREM 62 4.1.1.1 RELATIONSHIP AND LOGICAL OPERATION OF RANDOM EVENT 62 4.1.1.2 AXIOMIZATION DEFINITION OF PROBABILITY 63 4.1.1.3 CONDITIONAL PROBABILITY AND BAYES THEOREM 64 4.1.2 PROBABILITY DISTRIBUTION FUNCTION 65 4.1.3 NORMAL DISTRIBUTION 67 4.1.3.1 THE DEFINITION AND PROPERTIES OF NORMAL DISTRIBUTION 67 4.1.3.2 MULTIDIMENSIONAL NORMAL DISTRIBUTION 69 4.1.4 LAWS OF LARGE NUMBERS AND CENTRAL LIMIT THEOREM 70 4.1.4.1 LAWS OF LARGE NUMBERS 70 4.1.4.2 CENTRAL LIMIT THEOREM 71 4.1.5 POWER LAW DISTRIBUTION 73 4.1.6 ENTROPY 74 4.2 FUZZY SET THEORY 76 4.2.1 MEMBERSHIP DEGREE AND MEMBERSHIP FUNCTION 76 4.2.2 DECOMPOSITION THEOREM AND EXPANDED PRINCIPLE 78 4.2.3 FUZZY RELATION 79 4.2.4 POSSIBILITY MEASURE 81 IMAGE 4 4.3 ROUGH SETTHEORY 81 4.3.1 IMPRECISE CATEGORY AND ROUGH SET 82 4.3.2 CHARACTERISTICS OF ROUGH SETS 84 4.3.3 ROUGH RELATIONS 86 4.4 CHAOS AND FRACTAL 89 4.4.1 BASIC CHARACTERISTICS OF CHAOS 90 4.4.2 STRANGE ATTRACTORS OF CHAOS 92 4.4.3 GEOMETRIE CHARACTERISTICS OF CHAOS AND FRACTAL 93 4.5 KERNEL FUNCTIONS AND PRINCIPAL CURVES 94 4.5.1 KERNEL FUNCTIONS 94 4.5.2 SUPPORT VECTOR MACHINE 97 4.5.3 PRINCIPAL CURVES 100 REFERENCES 104 CHAPTER 5 QUALITATIVE AND QUANTITATIVE TRANSFORM MODEL - CLOUD MODEL 107 5.1 PERSPECTIVES ON THE STUDY OF AI WITH UNCERTAINTY 107 5.1.1 MULTIPLE PERSPECTIVES ON THE STUDY OF HUMAN INTELLIGENCE 107 5.1.2 THE IMPORTANCE OF CONCEPTS IN NATURAL LANGUAGES 110 5.1.3 THE RELATIONSHIP BETWEEN RANDOMNESS AND FUZZINESS IN A CONCEPT 110 5.2 REPRESENTING CONCEPTS USING CLOUD MODELS 112 5.2.1 CLOUD AND CLOUD DROP 112 5.2.2 NUMERICAL CHARACTERISTICS OF CLOUD 113 5.2.3 TYPES OF CLOUD MODEL 115 5.3 NORMAL CLOUD GENERATOR 118 5.3.1 FORWARD CLOUD GENERATOR 118 5.3.2 CONTRIBUTIONS OF CLOUD DROPS TO A CONCEPT 123 5.3.3 UNDERSTANDING THE LUNAR CALENDAR S SOLAR TERMS THROUGH CLOUD MODELS 124 5.3.4 BACKWARD CLOUD GENERATOR 125 5.3.5 PRECISION ANALYSIS OF BACKWARD CLOUD GENERATOR 132 5.3.6 MORE ON UNDERSTANDING NORMAL CLOUD MODEL 133 5.4 MATHEMATICAL PROPERTIES OF NORMAL CLOUD 138 5.4.1 STATISTICAL ANALYSIS OF THE CLOUD DROPS DISTRIBUTION 138 5.4.2 STATISTICAL ANALYSIS OF THE CLOUD DROPS CERTAINTY DEGREE 140 5.4.3 EXPECTATION CURVES OF NORMAL CLOUD 142 5.5 ON THE PERVASIVENESS OF THE NORMAL CLOUD MODEL 144 5.5.1 PERVASIVENESS OF NORMAL DISTRIBUTION 144 5.5.2 PERVASIVENESS OF BELL MEMBERSHIP FUNCTION 145 5.5.3 SIGNIFICANCE OF NORMAL CLOUD 148 REFERENCES 150 IMAGE 5 CHAPTER 6 DISCOVERING KNOWLEDGE WITH UNCERTAINTY THROUGH METHODOLOGIES IN PHYSICS 153 6.1 FROM PERCEPTION OF PHYSICAL WORLD TO PERCEPTION OF HUMAN SEIF 153 6.1.1 EXPRESSING CONCEPTS BY USING ATOM MODELS 154 6.1.2 DESCRIBING INTERACTION BETWEEN OBJECTS BY USING FIELD 155 6.1.3 DESCRIBING HIERARCHICAL STRUCTURE OF KNOWLEDGE BY USING GRANULARITY 156 6.2 DATA FIELD 158 6.2.1 FROM PHYSICAL FIELD TO DATA FIELD 158 6.2.2 POTENTIAL FIELD AND FORCE FIELD OF DATA 160 6.2.3 INFLUENCE COEFFICIENT OPTIMIZATION OF FIELD FUNCTION 172 6.2.4 DATA FIELD AND VISUAL THINKING SIMULATION 178 6.3 UNCERTAINTY IN CONCEPT HIERARCHY 182 6.3.1 DISCRETIZATION OF CONTINUOUS DATA 183 6.3.2 VIRTUAL PAN CONCEPT TREE 186 6.3.3 CLIMBING-UP STRATEGY AND ALGORITHMS 188 6.4 KNOWLEDGE DISCOVERY STATE SPACE 196 6.4.1 THREE KINDS OF STATE SPACES 196 6.4.2 STATE SPACE TRANSFORMATION 197 6.4.3 MAJOR OPERATIONS IN STATE SPACE TRANSFORMATION 199 REFERENCES 200 CHAPTER 7 DATA MINING FOR DISCOVERING KNOWLEDGE WITH UNCERTAINTY 201 7.1 UNCERTAINTY IN DATA MINING 201 7.1.1 DATA MINING AND KNOWLEDGE DISCOVERY 201 7.1.2 UNCERTAINTY IN DATA MINING PROCESS 202 7.1.3 UNCERTAINTY IN DISCOVERED KNOWLEDGE 204 7.2 CLASSIFICATION AND CLUSTERING WITH UNCERTAINTY 205 7.2.1 CLOUD CLASSIFICATION 206 7.2.2 CLUSTERING BASED ON DATA FIELD 213 7.2.3 OUTLIER DETECTION AND DISCOVERY BASED ON DATA FIELD 238 7.3 DISCOVERY OF ASSOCIATION RULES WITH UNCERTAINTY 244 7.3.1 RECONSIDERATION OF THE TRADITIONAL ASSOCIATION RULES 244 7.3.2 ASSOCIATION RULE MINING AND FORECASTING 247 7.4 TIME SERIES DATA MINING AND FORECASTING 253 7.4.1 TIME SERIES DATA MINING BASED ON CLOUD MODELS 255 7.4.2 STOCK DATA FORECASTING 256 REFERENCES 269 CHAPTER 8 REASONING AND CONTROL OF QUALITATIVE KNOWLEDGE 273 8.1 QUALITATIVE RULE CONSTRUCTION BY CLOUD 273 8.1.1 PRECONDITION CLOUD GENERATOR AND POSTCONDITION CLOUD GENERATOR 273 IMAGE 6 8.1.2 RULE GENERATOR 276 8.1.3 FROM CASES TO RULE GENERATION 279 8.2 QUALITATIVE CONTROL MECHANISM 280 8.2.1 FUZZY, PROBABILITY, AND CLOUD CONTROL METHODS 280 8.2.2 THEORETIC EXPLANATION OF MAMDANI FUZZY CONTROL METHOD 289 8.3 INVERTED PENDULUM - AN EXAMPLE OF INTELLIGENT CONTROL WITH UNCERTAINTY 291 8.3.1 INVERTED PENDULUM SYSTEM AND ITS CONTROL 291 8.3.2 INVERTED PENDULUM QUALITATIVE CONTROL MECHANISM 292 8.3.3 CLOUD CONTROL POLICY OF TRIPLE-LINK INVERTED PENDULUM 294 8.3.4 BALANCING PATTERNS OF AN INVERTED PENDULUM 302 REFERENCES 312 CHAPTER 9 A NEW DIRECTION FOR AI WITH UNCERTAINTY 315 9.1 COMPUTING WITH WORDS 316 9.2 STUDY OF COGNITIVE PHYSICS 320 9.2.1 EXTENSION OF CLOUD MODEL 320 9.2.2 DYNAMIC DATA FIELD 323 9.3 COMPLEX NETWORKS WITH SMALL WORLD AND SCALE-FREE MODELS 328 9.3.1 REGULARITY OF UNCERTAINTY IN COMPLEX NETWORKS 329 9.3.2 SCALE-FREE NETWORKS GENERATION 332 9.3.3 APPLICATIONS OF DATA FIELD THEORY TO NETWORKED INTELLIGENCE 339 9.4 LONG WAY TO GO FOR AI WITH UNCERTAINTY 340 9.4.1 LIMITATIONS OF COGNITIVE PHYSICS METHODOLOGY 340 9.4.2 DIVERGENCES FROM DANIEL, THE NOBEL ECONOMICS PRIZE WINNER 340 REFERENCES 342 RESEARCH FOUNDATION SUPPORT 345 INDEX 347
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spellingShingle	Li, Deyi Du, Yi Artificial intelligence with uncertainty Artificial intelligence Uncertainty (Information theory) Künstliche Intelligenz Künstliche Intelligenz (DE-588)4033447-8 gnd Entscheidung bei Unsicherheit (DE-588)4070864-0 gnd Unsicherheit (DE-588)4186957-6 gnd
subject_GND	(DE-588)4033447-8 (DE-588)4070864-0 (DE-588)4186957-6
title	Artificial intelligence with uncertainty
title_auth	Artificial intelligence with uncertainty
title_exact_search	Artificial intelligence with uncertainty
title_full	Artificial intelligence with uncertainty Deyi Li and Yi Du
title_fullStr	Artificial intelligence with uncertainty Deyi Li and Yi Du
title_full_unstemmed	Artificial intelligence with uncertainty Deyi Li and Yi Du
title_short	Artificial intelligence with uncertainty
title_sort	artificial intelligence with uncertainty
topic	Artificial intelligence Uncertainty (Information theory) Künstliche Intelligenz Künstliche Intelligenz (DE-588)4033447-8 gnd Entscheidung bei Unsicherheit (DE-588)4070864-0 gnd Unsicherheit (DE-588)4186957-6 gnd
topic_facet	Artificial intelligence Uncertainty (Information theory) Künstliche Intelligenz Entscheidung bei Unsicherheit Unsicherheit
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