Contemporary artificial intelligence:
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Boca Raton, FL
Taylor & Francis
2013
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| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Item Description: | Includes bibliographical references and index |
| Physical Description: | XIII, 501 S. Ill., graph. Darst. |
| ISBN: | 9781439844694 |
Staff View
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Record in the Search Index
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| adam_text | Titel: Contemporary artificial intelligence
Autor: Neapolitan, Richard E
Jahr: 2013
Contents
Preface xi
About the Authors xiii
1 Introduction to Artificial Intelligence 1
1.1 History of Artificial Intelligence................... 2
1.1.1 What Is Artificial Intelligence?............... 2
1.1.2 Emergence of AI....................... 4
1.1.3 Cognitive Science and AI.................. 4
1.1.4 Logical Approach to AI................... 5
1.1.5 Knowledge-Based Systems.................. 6
1.1.6 Probabilistic Approach to AI................ 7
1.1.7 Evolutionary Computation and Swarm Intelligence .... 7
1.1.8 A Return to Creating HAL................. 8
1.2 Contemporary Artificial Intelligence................ 8
1 Logical Intelligence 11
2 Propositional Logic 13
2.1 Basics of Propositional Logic.................... 14
2.1.1 Syntax............................. 14
2.1.2 Semantics........................... 16
2.1.3 Tautologies and Logical Implication............ 20
2.1.4 Logical Arguments...................... 22
2.1.5 Derivation Systems...................... 25
2.2 Resolution............................... 28
2.2.1 Normal Forms ........................ 28
2.2.2 Derivations Using Resolution................ 31
2.2.3 Resolution Algorithm.................... 34
2.3 Artificial Intelligence Applications ................. 35
2.3.1 Knowledge-Based Systems.................. 35
2.3.2 Wumpus World........................ 48
2.4 Discussion and Further Reading................... 56
vi CONTENTS
3 First-Order Logic 61
3.1 Basics of First-Order Logic ..................... 62
3.1.1 Syntax............................. 62
3.1.2 Semantics........................... 65
3.1.3 Validity and Logical Implication.............. 69
3.1.4 Derivation Systems...................... 71
3.1.5 Modus Ponens for First-Order Logic............ 75
3.2 Artificial Intelligence Applications ................. 79
3.2.1 Wumpus World Revisited.................. 79
3.2.2 Planning ........................... 80
3.3 Discussion and Further Reading................... 84
4 Certain Knowledge Representation 89
4.1 Taxonomic Knowledge........................ 90
4.1.1 Semantic Nets ........................ 90
4.1.2 Model of Human Organization of Knowledge....... 92
4.2 Frames................................. 92
4.2.1 Frame Data Structure.................... 92
4.2.2 Planning a Trip Using Frames................ 93
4.3 Nonmonotonic Logic......................... 96
4.3.1 Circumscription ....................... 96
4.3.2 Default Logic......................... 97
4.3.3 Difficulties........................... 99
4.4 Discussion and Further Reading................... 99
II Probabilistic Intelligence 101
5 Probability 103
5.1 Probability Basics ..........................106
5.1.1 Probability Spaces......................106
5.1.2 Conditional Probability and Independence.........109
5.1.3 Bayes Theorem .......................112
5.2 Random Variables..........................113
5.2.1 Probability Distributions of Random Variables......113
5.2.2 Independence of Random Variables.............118
5.3 Meaning of Probability........................122
5.3.1 Relative Frequency Approach to Probability........122
5.3.2 Subjective Approach to Probability.............125
5.4 Random Variables in Applications.................127
5.5 Probability in the Wumpus World.................131
6 Uncertain Knowledge Representation 137
6.1 Intuitive Introduction to Bayesian Networks............139
6.2 Properties of Bayesian Networks..................142
6.2.1 Definition of a Bayesian Network..............142
6.2.2 Representation of a Bayesian Network...........145
CONTENTS vii
6.3 Causal Networks as Bayesian Networks...............148
6.3.1 Causality...........................148
6.3.2 Causality and the Markov Condition............149
6.3.3 Markov Condition without Causality............154
6.4 Inference in Bayesian Networks...................154
6.4.1 Examples of Inference.................... 155
6.4.2 Inference Algorithms and Packages............. 157
6.4.3 Inference Using Netica.................... 159
6.5 Networks with Continuous Variables................ 161
6.5.1 Gaussian Bayesian Networks ................161
6.5.2 Hybrid Networks.......................163
6.6 Obtaining the Probabilities.....................165
6.6.1 Difficulty Inherent in Multiple Parents...........166
6.6.2 Basic Noisy OR-Gate Model ................167
6.6.3 Leaky Noisy OR-Gate Model................169
6.6.4 Further Models........................171
6.7 Large-Scale Application: Promedas.................171
7 Advanced Properties of Bayesian Network 177
7.1 Entailed Conditional Independendes................178
7.1.1 Examples of Entailed Conditional Independencies.....178
7.1.2 d-Separation .........................182
7.2 Faithfulness..............................185
7.2.1 Unfaithful Probability Distributions............186
7.2.2 Faithfulness Condition....................188
7.3 Markov Equivalence.........................189
7.4 Markov Blankets and Boundaries..................192
8 Decision Analysis 199
8.1 Decision Trees............................. 200
8.1.1 Simple Examples....................... 200
8.1.2 Solving More Complex Decision Trees........... 204
8.2 Influence Diagrams.......................... 217
8.2.1 Representing with Influence Diagrams........... 217
8.2.2 Solving Influence Diagrams................ . 225
8.2.3 Techniques for Solving Influence Diagrams......... 225
8.2.4 Solving Influence Diagrams Using Netica.......... 229
8.3 Modeling Risk Preferences...................... 234
8.3.1 Exponential Utility Function................ 234
8.3.2 Assessing r.......................... 236
8.4 Analyzing Risk Directly....................... 237
8.4.1 Using the Variance to Measure Risk............ 237
8.4.2 Risk Profiles ......................... 239
8.4.3 Dominance.......................... 240
8.5 Good Decision versus Good Outcome................ 245
8.6 Sensitivity Analysis.......................... 245
8.7 Value of Information......................... 248
viii CONTENTS
8.7.1 Expected Value of Perfect Information...........249
8.7.2 Expected Value of Imperfect Information .........252
8.8 Discussion and Further Reading...................253
8.8.1 Academics...........................254
8.8.2 Business and Finance ....................254
8.8.3 Capital Equipment......................254
8.8.4 Computer Games.......................254
8.8.5 Computer Vision.......................254
8.8.6 Computer Software......................255
8.8.7 Medicine ...........................255
8.8.8 Natural Language Processing................256
8.8.9 Planning ...........................256
8.8.10 Psychology..........................256
8.8.11 Reliability Analysis......................256
8.8.12 Scheduling ..........................256
8.8.13 Speech Recognition......................256
8.8.14 Vehicle Control and Malfunction Diagnosis........257
III Emergent Intelligence 265
9 Evolutionary Computation 267
9.1 Genetics Review...........................268
9.2 Genetic Algorithms..........................270
9.2.1 Algorithm...........................271
9.2.2 Illustrative Example.....................271
9.2.3 Traveling Salesperson Problem...............274
9.3 Genetic Programming........................284
9.3.1 Illustrative Example.....................285
9.3.2 Artificial Ant.........................288
9.3.3 Application to Financial Trading..............291
9.4 Discussion and Further Reading...................294
10 Swarm Intelligence 297
10.1 Ant System..............................298
10.1.1 Real Ant Colonies......................298
10.1.2 Artificial Ants for Solving the TSP.............298
10.2 Flocks.................................302
10.3 Discussion and Further Reading...................306
IV Learning 309
11 Learning Deterministic Models 311
11.1 Supervised Learning.........................312
11.2 Regression...............................312
11.2.1 Simple Linear Regression..................312
CONTENTS ix
11.2.2 Multiple Linear Regression................. 315
11.2.3 Overfitting and Cross Validation.............. 317
11.3 Learning a Decision Tree....................... 319
11.3.1 Information Theory ..................... 321
11.3.2 Information Gain and the ID3 Algorithm......... 323
11.3.3 Overfitting.......................... 326
12 Learning Probabilistic Model Parameters 331
12.1 Learning a Single Parameter..................... 332
12.1.1 Binomial Random Variables................. 332
12.1.2 Multinomial Random Variables............... 335
12.2 Learning Parameters in a Bayesian Network............ 336
12.2.1 Procedure for Learning Parameters............. 337
12.2.2 Equivalent Sample Size ................... 339
12.3 Learning Parameters with Missing Data*............. 341
13 Learning Probabilistic Model Structure 351
13.1 Structure Learning Problem..................... 352
13.2 Score-Based Structure Learning................... 353
13.2.1 Bayesian Score........................ 353
13.2.2 BIC Score........................... 360
13.2.3 Consistent Scoring Criteria................. 362
13.2.4 How Many DAGs Must We Score?............. 362
13.2.5 Using the Learned Network to Do Inference........ 363
13.2.6 Learning Structure with Missing Data*.......... 364
13.2.7 Approximate Structure Learning.............. 373
13.2.8 Model Averaging....................... 377
13.2.9 Approximate Model Averaging*.............. 380
13.3 Constraint-Based Structure Learning................ 383
13.3.1 Learning a DAG Faithful to P............... 383
13.3.2 Learning a DAG in which P Is Embedded Faithfully ... 389
13.4 Application: MENTOR....................... 390
13.4.1 Developing the Network................... 390
13.4.2 Validating MENTOR.................... 394
13.5 Software Packages for Learning................... 394
13.6 Causal Learning ........................... 394
13.6.1 Causal Faithfulness Assumption .............. 395
13.6.2 Causal Embedded Faithfulness Assumption........ 397
13.6.3 Application: College Student Retention Rate....... 401
13.7 Class Probability Trees........................ 404
13.7.1 Theory of Class Probability Trees ............. 405
13.7.2 Application to Targeted Advertising............ 407
13.8 Discussion and Further Reading................... 410
13.8.1 Biology............................ 410
13.8.2 Business and Finance.................... 411
13.8.3 Causal Learning . ...................... 411
13.8.4 Data Mining......................... 411
x CONTENTS
13.8.5 Medicine ...........................411
13.8.6 Weather Forecasting.....................412
14 More Learning 417
14.1 Unsupervised Learning........................417
14.1.1 Clustering...........................418
14.1.2 Automated Discovery....................419
14.2 Reinforcement Learning.......................420
14.2.1 Multi-Armed Bandit Algorithms..............420
14.2.2 Dynamic Networks*.....................423
14.3 Discussion and Further Readme...................436
V Language Understanding 439
15 Natural Language Understanding 441
15.1 Parsing ................................442
15.1.1 Recursive Parser.......................445
15.1.2 Ambiguity...........................447
15.1.3 Dynamic Programming Parser ...............449
15.1.4 Probabilistic Parser .....................454
15.1.5 Obtaining Probabilities for a PCFG............456
15.1.6 Lexicalized PCFG......................456
15.2 Semantic Interpretation.......................458
15.3 Concept/Knowledge Interpretation.................460
15.4 Information Extraction........................461
15.4.1 Applications of Information Extraction...........462
15.4.2 Architecture for an Information Extraction System .... 463
15.5 Discussion and Further Reading...................464
Bibliography 467
Index 493
|
| any_adam_object | 1 |
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| spelling | Neapolitan, Richard E. Verfasser (DE-588)141964383 aut Contemporary artificial intelligence Richard E. Neapolitan ; Xia Jiang Boca Raton, FL Taylor & Francis 2013 XIII, 501 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Includes bibliographical references and index Künstliche Intelligenz (DE-588)4033447-8 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Künstliche Intelligenz (DE-588)4033447-8 s Maschinelles Lernen (DE-588)4193754-5 s 1\p DE-604 Jiang, Xia Verfasser (DE-588)133055531 aut HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025293156&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
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| title | Contemporary artificial intelligence |
| title_auth | Contemporary artificial intelligence |
| title_exact_search | Contemporary artificial intelligence |
| title_full | Contemporary artificial intelligence Richard E. Neapolitan ; Xia Jiang |
| title_fullStr | Contemporary artificial intelligence Richard E. Neapolitan ; Xia Jiang |
| title_full_unstemmed | Contemporary artificial intelligence Richard E. Neapolitan ; Xia Jiang |
| title_short | Contemporary artificial intelligence |
| title_sort | contemporary artificial intelligence |
| topic | Künstliche Intelligenz (DE-588)4033447-8 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
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