Computation and Human Experience (Learning in Doing)

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Computation and Human Experience (Learning in Doing)

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  • 製本 Hardcover:ハードカバー版/ページ数 371 p.
  • 言語 ENG
  • 商品コード 9780521384322
  • DDC分類 006.301

基本説明

Offers a critical reconstruction of the fundamental ideas and methods of artificial intelligence research.

Full Description


This book offers a critical reconstruction of the fundamental ideas and methods of artificial intelligence research. Through close attention to the metaphors of artificial intelligence and their consequences for the field's patterns of success and failure, it argues for a reorientation of the field away from thought in the head and towards activity in the world. By considering computational ideas in a large, philosophical framework, the author eases critical dialogue between technology and the social sciences. AI can benefit from an understanding of the field in relation to human nature, and in return, it offers a powerful mode of investigation into the practicalities of physical realization.

Table of Contents

Preface                                            xi
1 Introduction 1 (26)
Activity 1 (4)
Planning 5 (5)
Why build things? 10 (6)
How computation explains 16 (4)
Critical orientation 20 (4)
Outline 24 (3)
2 Metaphor in practice 27 (22)
Levels of analysis 27 (1)
Language in practice 28 (5)
Metaphors in technical work 33 (5)
Centers and margins 38 (6)
Margins in practice 44 (5)
3 Machinery and dynamics 49 (17)
Mentalism 49 (3)
Interactionism 52 (5)
Machinery and dynamics 57 (4)
Interactionist methodology 61 (5)
4 Abstraction and implementation 66 (23)
Structures of computation 66 (5)
A case study: variables 71 (9)
Architectural and generative reasoning 80 (5)
Generative reasoning and mentalism 85 (4)
5 The digital abstraction 89 (16)
Digital logic 89 (3)
The meaning of circuitry 92 (4)
The temporality of computation 96 (7)
Embodied computation 103(2)
6 Dependency maintenance 105(19)
Critical technical practice 105(2)
About routines 107(6)
Main ideas of dependency maintenance 113(11)
7 Rule system 124(18)
Using dependencies in a rule system 124(2)
Rule language semantics 126(4)
How it works 130(6)
Incremental updating 136(3)
Advanced rule writing 139(3)
8 Planning and improvisation 142(18)
The idea of planning 142(3)
Troubles with planning 145(5)
Planning and execution 150(6)
Improvisation 156(4)
9 Running arguments 160(19)
From plans to arguments 160(6)
Argument and centralization 166(5)
How running arguments work 171(8)
10 Experiments with running arguments 179(43)
Motivation 179(2)
Demonstration 181(10)
Patterns of transfer 191(7)
Transfer and goal structure 198(16)
Analysis 214(8)
11 Representation and indexicality 222(19)
World models 222(5)
Knowledge representation 227(3)
Indexicality 230(4)
Intentionality 234(7)
12 Deictic representation 241(19)
Deictic intentionality 241(5)
Causal relationships 246(4)
Entities and aspects 250(5)
Using deictic representation 255(5)
13 Pengi 260(42)
Argument 260(3)
Pengo and Pengi 263(3)
Entities and aspects in Pengi 266(2)
How Pengi decides what to do 268(5)
Architecture 273(4)
Visual system 277(8)
Central system 285(3)
Example 288(7)
Seriality and focus 295(2)
Questions and answers 297(5)
14 Conclusion 302(14)
Discourse and practice 302(5)
Converging computational work 307(5)
Some next steps 312(4)
Notes 316(18)
References 334(27)
Author index 361(5)
Subject index 366