Computation and Intelligence: Collected Readings

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Computation and Intelligence brings together 29 readings in Artificial Intelligence that are particularly relevant to today's student/practitioner. With its helpful critique of the selections, extensive bibliography, and clear presentation of the material, Computation andIntelligence will be a useful adjunct to any course in AI as well as a handy reference for professionals in the field.The book is divided into five parts, each reflecting the stages of development of AI. The first part, Foundations,, contains ...

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Overview

Computation and Intelligence brings together 29 readings in Artificial Intelligence that are particularly relevant to today's student/practitioner. With its helpful critique of the selections, extensive bibliography, and clear presentation of the material, Computation andIntelligence will be a useful adjunct to any course in AI as well as a handy reference for professionals in the field.The book is divided into five parts, each reflecting the stages of development of AI. The first part, Foundations,, contains readings that present or discuss foundational ideas linking computation and intelligence, typified by A. M. Turing's "ComputingMachinery and Intelligence." The second part, Knowledge Representation, presents a sampling of numerous representational schemes by Newell, Minsky, Collins & Quillian, Winograd, Schank,Hayes, Holland, McClelland, Rumelhart, Hinton, and Brooks.The third part, Weak Method ProblemSolving, fouses on the research and design of syntax-based problem solvers, including the most famous of these, the Logic Theorist and GPS. The fourth part, Reasoning in Complex and DynamicEnvironments, presents a broad spectrum of the AI community's research in knowledge-intensive problem solving, from McCarthy's early design of systems with "common sense" to model-based reasoning.The two concluding selections, by Marvin Minsky and by Herbert Simon, respectively,present the recent thoughts of two of AI's pioneers who revisit the concepts and controversies that have developed during the evolution of the tools and techniques that make up the current practice ofArtificial Intelligence.

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Table of Contents

Acknowledgments
Introduction
1 The Prehistory of Android Epistemology 3
2 Computing Machinery and Intelligence 23
3 Steps Towards Artificial Intelligence 47
4 Computer Science as Empirical Enquiry: Symbols and Search 91
5 The Knowledge Level 125
6 A Framework for Representing Knowledge 165
7 Retrieval Time from Semantic Memory 193
8 A Procedural Model of Language Understanding 203
9 The Structure of Episodes in Memory 235
10 In Defense of Logic 261
11 Escaping Brittleness: The Possibility of General Purpose Learning Algorithms Applied to Parallel Rule-Based Systems 275
12 The Appeal of Parallel Distributed Processing 305
13 Intelligence Without Representation 343
14 Empirical Explorations with the Logic Theory Machine 365
15 Some Studies in Machine Learning Using the Game of Checkers 391
16 GPS, A Program that Simulates Human Thought 415
17 STRIPS: A New Approach in the Application of Theorem Proving to Problem Solving 429
18 Blackboard Systems: The Blackboard Model of Problem Solving and the Evolution of Blackboard Architectures 447
19 Programs with Common Sense 479
20 Dendral and Meta-Dendral: Their Application Dimension 493
21 Consultation Systems for Physicians 511
22 A Truth Maintenance System 529
23 Circumscription - A Form of Non-Monotonic Reasoning 555
24 The Second Naive Physics Manifesto 567
25 Paradigms for Machine Learning 587
26 Improving Human Decision Making Through Case Based Decision Aiding 597
27 Diagnosis Based on Description of Structure and Function 623
28 Pengi: An Implementation of a Theory of Activity 635
29 Logical Versus Analogical or Symbolic Versus Connectionist or Neat Versus Scruffy 647
30 Machine as Mind 675
Bibliography 693
Index 729
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