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Preface | p. xiii |
Beginnings | p. 1 |
Dreams and Dreamers | p. 3 |
Clues | p. 10 |
From Philosophy and Logic | p. 10 |
From Life Itself | p. 15 |
From Engineering | p. 25 |
Early Explorations: 1950s and 1960s | p. 47 |
Gatherings | p. 49 |
Session on Learning Machines | p. 49 |
The Dartmouth Summer Project | p. 52 |
Mechanization of Thought Processes | p. 56 |
Pattern Recognition | p. 62 |
Character Recognition | p. 62 |
Neural Networks | p. 64 |
Statistical Methods | p. 73 |
Applications of Pattern Recognition to Aerial Reconnaissance | p. 74 |
Early Heuristic Programs | p. 81 |
The Logic Theorist and Heuristic Search | p. 81 |
Proving Theorems in Geometry | p. 85 |
The General Problem Solver | p. 87 |
Game-Playing Programs | p. 89 |
Semantic Representations | p. 96 |
Solving Geometric Analogy Problems | p. 96 |
Storing Information and Answering Questions | p. 98 |
Semantic Networks | p. 100 |
Natural Language Processing | p. 103 |
Linguistic Levels | p. 103 |
Machine Translation | p. 107 |
Question Answering | p. 110 |
1960s' Infrastructure | p. 114 |
Programming Languages | p. 114 |
Early AI Laboratories | p. 115 |
Research Support | p. 118 |
All Dressed Up and Places to Go | p. 120 |
Efflorescence: MID-1960s to MID-1970s | p. 123 |
Computer Vision | p. 125 |
Hints from Biology | p. 126 |
Recognizing Faces | p. 127 |
Computer Vision of Three-Dimensional Solid Objects | p. 128 |
“Hand–Eye” Research | p. 141 |
At MIT | p. 141 |
At Stanford | p. 142 |
In Japan | p. 145 |
Edinburgh's “FREDDY” | p. 145 |
Knowledge Representation and Reasoning | p. 149 |
Deductions in Symbolic Logic | p. 149 |
The Situation Calculus | p. 152 |
Logic Programming | p. 153 |
Semantic Networks | p. 154 |
Scripts and Frames | p. 156 |
Mobile Robots | p. 162 |
Shakey, the SRI Robot | p. 162 |
The Stanford Cart | p. 176 |
Progress in Natural Language Processing | p. 181 |
Machine Translation | p. 181 |
Understanding | p. 182 |
Game Playing | p. 193 |
The Dendral Project | p. 197 |
Conferences, Books, and Funding | p. 202 |
Applications and Specializations: 1970s to early 1980s | p. 207 |
Speech Recognition and Understanding Systems | p. 209 |
Speech Processing | p. 209 |
The Speech Understanding Study Group | p. 211 |
The DARPA Speech Understanding Research Program | p. 212 |
Subsequent Work in Speech Recognition | p. 220 |
Consulting Systems | p. 224 |
The SRI Computer-Based Consultant | p. 224 |
Expert Systems | p. 229 |
Understanding Queries and Signals | p. 244 |
The Setting | p. 244 |
Natural Language Access to Computer Systems | p. 247 |
HASP/SIAP | p. 252 |
Progress in Computer Vision | p. 258 |
Beyond Line-Finding | p. 258 |
Finding Objects in Scenes | p. 262 |
DARPA's Image Understanding Program | p. 267 |
Boomtimes | p. 271 |
“New-Generation” projects | p. 275 |
The Japanese Create a Stir | p. 277 |
The Fifth-Generation Computer Systems Project | p. 277 |
Some Impacts of the Japanese Project | p. 281 |
DARPA's Strategic Computing Program | p. 286 |
The Strategic Computing Plan | p. 286 |
Major Projects | p. 289 |
AI Technology Base | p. 294 |
Assessment | p. 297 |
Entr'acte | p. 303 |
Speed Bumps | p. 305 |
Opinions from Various Onlookers | p. 305 |
Problems of Scale | p. 319 |
Acknowledged Shortcomings | p. 325 |
The “AI Winter” | p. 327 |
Controversies and Alternative Paradigms | p. 331 |
About Logic | p. 331 |
Uncertainty | p. 332 |
“Kludginess” | p. 333 |
About Behavior | p. 334 |
Brain-Style Computation | p. 339 |
Simulating Evolution | p. 341 |
Scaling Back AIs Goals | p. 344 |
The Growing Armamentarium: From the 1980s Onward | p. 347 |
Reasoning and Representation | p. 349 |
Nonmonotonic or Defeasible Reasoning | p. 349 |
Qualitative Reasoning | p. 352 |
Semantic Networks | p. 354 |
Other Approaches to Reasoning and Representation | p. 365 |
Solving Constraint Satisfaction Problems | p. 365 |
Solving Problems Using Propositional Logic | p. 368 |
Representing Text as Vectors | p. 373 |
Latent Semantic Analysis | p. 376 |
Bayesian Networks | p. 381 |
Representing Probabilities in Networks | p. 381 |
Automatic Construction of Bayesian Networks | p. 387 |
Probabilistic Relational Models | p. 391 |
Temporal Bayesian Networks | p. 393 |
Machine Learning | p. 398 |
Memory-Based Learning | p. 398 |
Case-Based Reasoning | p. 400 |
Decision Trees | p. 402 |
Neural Networks | p. 408 |
Unsupervised Learning | p. 413 |
Reinforcement Learning | p. 415 |
Enhancements | p. 422 |
Natural Languages and Natural Scenes | p. 431 |
Natural Language Processing | p. 431 |
Computer Vision | p. 436 |
Intelligent System Architectures | p. 455 |
Computational Architectures | p. 456 |
Cognitive Architectures | p. 467 |
Modern AI: Today And Tomorrow | p. 479 |
Extraordinary Achievements | p. 481 |
Games | p. 481 |
Robot Systems | p. 488 |
Ubiquitous Artificial Intelligence | p. 501 |
AI at Home | p. 501 |
Advanced Driver Assistance Systems | p. 502 |
Route Finding in Maps | p. 503 |
You Might Also Like… | p. 503 |
Computer Games | p. 504 |
Smart Tools | p. 507 |
In Medicine | p. 507 |
For Scheduling | p. 509 |
For Automated Trading | p. 509 |
In Business Practices | p. 510 |
In Translating Languages | p. 511 |
For Automating Invention | p. 511 |
For Recognizing Faces | p. 512 |
The Quest Continues | p. 515 |
In the Labs | p. 516 |
Toward Human-Level Artificial Intelligence | p. 525 |
Summing Up | p. 534 |
Index | p. 539 |
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The Used, Rental and eBook copies of this book are not guaranteed to include any supplemental materials. Typically, only the book itself is included. This is true even if the title states it includes any access cards, study guides, lab manuals, CDs, etc.