偶然の発見<br>Chance Discovery (Advanced Information Processing) (2003. 400 p. w. 120 figs.)

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偶然の発見
Chance Discovery (Advanced Information Processing) (2003. 400 p. w. 120 figs.)

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  • 製本 Hardcover:ハードカバー版/ページ数 400 p., 120 illus.
  • 商品コード 9783540005490

基本説明

Contents: Part I: Chance Dicovery in the Complex Real World; Part II: Key 1 - Communications for Chance Discovery; Part III: Key 2 - Perceptions for Context-Shifting; Part IV: Key 3 - Computer-Aided Chance Discoveries; Part V: Keys Combined to Applications.

Full Description


Chance discovery means discovering chances - the breaking points in systems, the marketing windows in business, etc. It involves determining the significance of some piece of information about an event and then using this new knowledge in decision making. The techniques developed combine data mining methods for finding rare but important events with knowledge management, groupware, and social psychology. The reader will find many applications, such as finding information on the Internet, recognizing changes in customer behavior, detecting the first signs of an imminent earthquake, etc.This first book dedicated to chance discovery covers the state of the art in the theory and methods and examines typical scenarios, and it thus appeals to researchers working on new techniques and algorithms and also to professionals dealing with real-world applications.

Table of Contents

Part I Chance Discovery in the Complex Real        1  (70)
World
1. Modeling the Process of Chance Discovery
Yukio Ohsawa 2 (14)
1.1 Which Do You Want, a Rule or an 2 (1)
Opportunity?
1.2 Is Chance Discovery Itself an Event 3 (1)
or a Process'
1.3 From Knowledge Discovery to Chance 4 (7)
Discovery
1.3.1 The Human as a Main Component of 5 (2)
Chance Discovery
1.3.2 Communications, Key 1 for Chance 7 (2)
Discovery
1.3.3 Scenic Information for 9 (1)
Imagination, Key 2 for Chance Discovery
1.3.4 Data Modeling and Mining, Key 3 9 (1)
for Chance Discovery
1.3.5 Criteria for Evaluating a 10 (1)
Discovered Chance
1.4 Double-Helical Model of Chance 11 (2)
Discovery Process
1.5 Subsumption Architecture for Robust 13 (1)
Helices
1.6 A Tentative Conclusion 14 (1)
References 15 (1)
2. Decisions by Chance and on Chance:
Meanings of Chance in Recent News Stories
Fumiko Yoshikawa 16 (14)
2.1 Introduction 16 (1)
2.2 The Etymology of Chance 17 (4)
2.2.1 Meanings of Chance in ME and Its 17 (1)
Synonyms in OE
2.2.2 Definitions of Chance in the OED2 18 (3)
2.3 Meanings of Chance in Recent News 21 (6)
Stories
2.3.1 The Classificatory Criterion 21 (1)
2.3.2 Chance Meaning 'Opportunity' 22 (1)
2.3.3 Chance Meaning 'Probability' 23 (1)
2.3.4 Chance Meaning 'Fortune' 23 (4)
2.3.5 Chance Involving a Certain Degree 27 (1)
of Risk
2.4 Discussion and Conclusion 27 (2)
References 29 (1)
3. Prediction, Forecasting, and Chance
Discovery
Yutaka Matsuo 30 (14)
3.1 Introduction 30 (1)
3.2 Existing Method of Prediction and 31 (5)
Forecasting
3.2.1 Time-Series Prediction 31 (1)
3.2.2 ARMA Model 32 (1)
3.2.3 Pattern Recognition 33 (1)
3.2.4 Information Between the Past and 34 (1)
the Future
3.2.5 Data-Mining Methods 34 (2)
3.3 Difference between 36 (2)
Prediction/Forecasting and Chance
Discovery
3.3.1 Emphasis on Model/Variable 36 (1)
Creation and Discovery
3.3.2 Emphasis on Rare Events 37 (1)
3.3.3 Emphasis on Human and Computer 37 (1)
Interaction
3.3.4 Relevance of 38 (1)
Prediction/Forecasting and Chance
Discovery
3.4 Importance of Structural Information 38 (3)
for Rare Events
3.4.1 Small Worlds 38 (2)
3.4.2 Structural Importance 40 (1)
3.5 Conclusion 41 (1)
References 42 (2)
4. Self-organizing Complex Systems
Henrik Jeldtoft Jensen 44 (18)
4.1 Introduction 44 (1)
4.2 What Is a Self-Organizing Complex 45 (4)
System?
4.2.1 What Is a Critical System? 45 (1)
4.2.2 Scale-Free Systems 46 (2)
4.2.3 Self-organizing Critical Systems 48 (1)
4.3 Details of Two Models 49 (7)
4.3.1 The Bak-Tang-Wiesenfeld Sandpile 49 (2)
Model
4.3.2 The Tangled Nature Model 51 (5)
4.4 What Does a Chance Look Like When 56 (2)
There Is No Scale?
4.5 Predictability in Complex Systems 58 (1)
4.6 Chance Discovery as an Understanding 59 (1)
of the Common Good
4.7 Some Concluding Remarks 60 (1)
References 61 (1)
5. Anatomy of Rare Events in a Complex
Adaptive System
Paul Jefferies, David Lamper, and Neil F. 62 (10)
Johnson
5.1 Can We Deal with Large Changes 62 (1)
5.2 A Complex Adaptive System of 63 (1)
Competing Agents
5.3 Large Changes as Crashes in a Bruijn 64 (5)
Graph
5.4 Conclusions 69 (1)
References 69 (2)
Part II Key 1- Communications for Chance 71 (98)
Discovery
6. Human-to-Human Communication for Chance
Discovery in Business
Hiroko Shoji 72 (12)
6.1 Face-to-Face Communication for Chance 72 (5)
Discovery in Retail Business
6.1.1 Importance of Sales Communication 72 (2)
in a Society with Oversupply of
Merchandise
6.1.2 Purchasing Types and 74 (1)
Communication Patterns
6.1.3 Creative Communication for Chance 75 (2)
Discovery
6.2 Email-Based Communication for Chance 77 (5)
Discovery in an R&D Project
6.2.1 Importance of Communication in 77 (1)
Multi-participant Projects
6.2.2 Communication Support for Chance 78 (1)
Discovery with Mailing List
6.2.3 Applications to Actual Design and 79 (3)
Development Projects
6.3 Conclusion 82 (1)
References 83 (1)
7. Topic Diffusion in a Community
Naohiro Matsumura 84 (14)
7.1 Introduction 84 (1)
7.2 Background 85 (1)
7.3 Topic Diffusion in a Community 85 (3)
7.3.1 My Idea of Diffusing Influence 85 (2)
7.3.2 Measuring the Influence of 87 (1)
Comments
7.3.3 Measuring the Influence of 88 (1)
Participants
7.3.4 Measuring the Influence of Terms 88 (1)
7.4 Influence Diffusion Model (IDM) 88 (2)
7.4.1 IDM for Comments 89 (1)
7.4.2 IDM for Participants 89 (1)
7.4.3 IDM for Terms 89 (1)
7.5 Case Study 90 (4)
7.5.1 Discovery of Influential Comments 90 (2)
7.5.2 Discovery of Opinion Leaders 92 (2)
7.5.3 Discovery of Interesting Terms 94 (1)
7.6 Experimental Evaluations 94 (2)
7.7 Conclusion 96 (1)
References 96 (2)
8. Dimensional Representations of Knowledge
in an Online Community
Robert McArthur and Peter Bruza 98 (17)
8.1 Introduction 98 (3)
8.2 The Semantic Context Within Utterances 101(2)
8.3 Representation of Utterance 103(1)
8.3.1 Vector Creation 104(8)
8.4 Closing Remarks 112(1)
References 112(3)
9. Discovery of Tacit Knowledge and Topical
Ebbs and Flows Within the Utterances of an
Online Community
Robert McArthur and Peter Bruza 115(18)
9.1 Introduction 115(2)
9.1.1 Minkowski Function 115(1)
9.1.2 LSA 116(1)
9.1.3 Information Flow 116(1)
9.2 Illustration I: Discovering Tacit 117(5)
Knowledge
9.2.1 Data Set 117(1)
9.2.2 Representation of Data 118(1)
9.2.3 Methods Applied 119(1)
9.2.4 General Remarks 120(2)
9.3 Illustration II: The Ebb and Flow of 122(7)
'Meanings'
9.3.1 Data Set 124(1)
9.3.2 Method 125(1)
9.3.3 Representation 126(1)
9.3.4 Discussion 126(3)
9.4 Conclusions and Further Research 129(2)
References 131(2)
10. Agent Communications for Chance Discovery
Peter McBurney and Simon Parsons 133(17)
10.1 Introduction 133(1)
10.2 CDM Requirements 134(3)
10.3 Generic Agent Communications 137(5)
Languages
10.3.1 FIPA ACL 137(3)
10.3.2 Assessment of FIPA ACL 140(2)
10.4 Dialogue Game Protocols 142(4)
10.4.1 Formal Dialogue Games 143(2)
10.4.2 Assessment of Dialogue Games 145(1)
10.5 Conclusions 146(1)
References 146(4)
11. Logics of Argumentation for Chance
Discovery
Simon Parsons and Peter McBurney 150(20)
11.1 Introduction 150(2)
11.2 Philosophical Background 152(1)
11.3 Argumentation and Dialogue 153(4)
11.3.1 Languages and Argumentation 153(1)
11.3.2 Inter-agent Argumentation 154(1)
11.3.3 Argumentation at All Levels 155(1)
11.3.4 Dialogue Games 156(1)
11.4 A System for Argumentation-Based 157(7)
Communication
11.4.1 A System for Internal 157(2)
Argumentation
11.4.2 Arguments Between Agents 159(2)
11.4.3 Rationality and Protocol 161(3)
11.5 Argument Aggregation 164(1)
11.6 Summary 164(1)
References 165(4)
Part III Key 2 - Perceptions for Context 169(80)
Shifting
12. Awareness and Imagination of Hidden
Factors and Rare Events
Yasufumi Takama 170(14)
12.1 Introduction 170(1)
12.2 Interaction with Environment for 171(1)
Chance Discovery
12.3 Web as Information Environment for 172(1)
Chance Discovery
12.4 Visualizing Topic Distribution on Web 173(1)
12.5 Plastic Clustering Method Based on 174(3)
Immune Network Model
12.5.1 Plastic Clustering Method 174(2)
12.5.2 Introduction of Memory Cell for 176(1)
Context Preservation
12.6 Experimental Results 177(4)
12.6.1 Topic Stream Extraction from 178(1)
Short Sequence
12.6.2 Topic Stream Extraction from 179(2)
Long Sequence
12.7 Conclusion 181(1)
References 182(2)
13. Effects of Scenic Information
Yasufumi Takama and Yukio Ohsawa 184(5)
13.1 Introduction: Scenic Information for 184(1)
Chance Discovery
13.2 Situation in Answering Questionnaires 184(1)
13.3 Making Diagram for Idea Generation 185(1)
13.4 Scenic Information on the Web 186(1)
13.5 Problem of Losing Scenic Information 186(1)
13.6 Media Technologies for Scenic 187(1)
Information
13.6.1 Integration of Real and 187(1)
Information Environments
13.6.2 Simulation and Visualization 187(1)
References 188(1)
14. The Storification of Chances
Helmut Prendinger and Mitsuru Ishizuka 189(19)
14.1 Introduction 189(2)
14.2 Motivating Example 191(2)
14.3 Observation vs. Immersion 193(2)
14.3.1 Inhabited Market Place 193(1)
14.3.2 Mission Rehearsal Exercise 194(1)
14.4 Life-Like Characters 195(2)
14.5 Approaches to Interactive Story 197(5)
Systems
14.5.1 Story-Morphing 197(1)
14.5.2 Plot Control in Interactive 198(1)
Stories
14.5.3 Interactive Drama with 198(1)
User-Controlled Character
14.5.4 Interactive Drama with Human 199(1)
Player
14.5.5 Story Telling with Anytime User 200(1)
Intervention
14.5.6 Story Nets 201(1)
14.5.7 Digital Director for Interactive 201(1)
Story Telling
14.6 Interactive Story Telling as a 202(3)
Business Training Environment
14.7 Summary and Conclusion 205(1)
References 206(2)
15. The Prepared Mind: the Role of
Representational Change in Chance Discovery
Eric Dietrich, Arthur B. Markman, C. Hunt 208(23)
Stilwell, and Michael Winkley
15.1 Introduction 208(1)
15.2 Analogical Reminding and Structure 209(2)
Mapping Theory: Background
15.3 Why Representational Change Is 211(5)
Needed to Understand Chance Discovery
15.4 Packing: an Overview 216(1)
15.5 STRANG: a Computational Model of 217(10)
Packing During Analogy Making
15.5.1 Background on the Operation of 217(3)
STRANG
15.5.2 Packing Irrelevant Attribute 220(4)
Information
15.5.3 Packing and Predicate Change 224(3)
15.6 Chance Discoveries and the Prepared 227(2)
Mind
15.7 Conclusion 229(1)
References 229(2)
16. AbductiOn and Analogy in Chance Discovery
Akinori Abe 231(19)
16.1 Introduction 231(1)
16.2 Definition of 'Chance' 232(1)
16.3 Abduction and Analogy as Discovery 233(4)
16.3.1 Reversed Deduction as Discovery 233(1)
16.3.2 Analogy as Discovery 234(3)
16.4 Computational Abduction 237(4)
16.4.1 Hypothetical Reasoning (Theorist) 237(3)
16.4.2 Clause Management System (CMS) 240(1)
16.5 Abductive Analogical Reasoning (AAR) 241(3)
16.6 Abductive Analogical Reasoning as 244(2)
Chance Discovery
16.6.1 Type 1: When Some of the 244(1)
Hypotheses Are Unknown
16.6.2 Type 2: When Some of the Rules 245(1)
Are Unknown
16.7 The Role of Abduction and Analogy in 246(1)
Chance Discovery
16.8 Conclusions 247(1)
References 247(2)
Part IV Key 3 - Computer-Aided Chance 249(54)
Discoveries
17. Active Mining with Visual Human Interface
Wataru Sunayama 250(12)
17.1 Introduction 250(1)
17.2 Features of Two-Dimensional 251(2)
Interfaces
17.2.1 Order by Directions 252(1)
17.2.2 Order by Arrangements 252(1)
17.2.3 Potential Relationships Among 252(1)
Objects
17.2.4 Cleared Relationships Among 252(1)
Objects
17.3 Information for Selection 253(3)
17.3.1 Necessity of Information 253(1)
17.3.2 Adaption of Information 253(1)
17.3.3 Effectiveness of Information 254(1)
17.3.4 Originality of Information 254(1)
17.3.5 Quality of Information 254(1)
17.3.6 Selection Criteria for Chance 254(2)
Discovery
17.4 Information-Visualization Systems 256(5)
for Chance Discovery
17.4.1 Interface 1: Display of Web 256(1)
Structures
17.4.2 Interface 2: Two-Dimensional 256(2)
Interface for Supplying Keywords
17.4.3 Interface 3: Indication of 258(2)
Relational Topics and Examples
17.4.4 Interface 4: Time Series of the 260(1)
Hit Numbers
17.5 Conclusions 261(1)
References 261(1)
18. KeyGraph: Visualized Structure Among
Event Clusters
Yukio Ohsawa 262(14)
18.1 KeyGraph for Abstracting Causalities 262(1)
in a Sequence
18.2 The Extensible Semantics of KeyGraph 263(2)
18.3 KeyGraph Applied to a Document 265(2)
18.4 KeyGraph Applied to POS Data 267(4)
18.5 Application to Web Links for 271(4)
Discovering Emerging Topics
18.6 Conclusions 275(1)
References 275(1)
19. Discovering Deep Building Blocks for
Competent Genetic Algorithms Using Chance
Discovery via KeyGraphs
David E. Goldberg, Kumara Sastry, and Yukio 276(28)
Ohsawa
19.1 Introduction 276(1)
19.2 GAs: Innovation, Competence, and 277(6)
Deep Building Blocks
19.2.1 The One-Minute Genetic 277(2)
Algorithmist
19.2.2 An Innovation Intuition for GAs 279(1)
19.2.3 Competent Genetic Algorithms 280(2)
19.2.4 The Problem of Deep Building 282(1)
Blocks
19.3 The KeyGraph Procedure: Overview and 283(2)
Intuition
19.3.1 An Innovation Intuition for the 283(1)
KeyGraph Procedure
19.3.2 The KeyGraph Procedure: an 284(1)
Overview
19.4 Can KeyGraphs Discover Deep Building 285(11)
Blocks?
19.4.1 Design of Pilot Experiments 286(3)
19.4.2 Competent GAs Fail to Identify 289(2)
Deep Building Blocks
19.4.3 KeyGraph and Identification of 291(1)
Low-Order Building Blocks
19.4.4 KeyGraph and Identification of 292(2)
Deep Building Blocks: a Naive Approach
19.4.5 Two Approaches for Identifying 294(2)
Deep Building Blocks
19.5 Future Work 296(1)
19.6 Conclusions 297(2)
References 299(4)
Part V Keys Combined to Applications 303(92)
20. Enhancing Daily Conversations
Yasuyuki Sumi and Kenji Mase 304(21)
20.1 Introduction 304(1)
20.2 AIDE: Augmented Informative 305(8)
Discussion Environment
20.2.1 System Overview of AIDE 305(3)
20.2.2 Mutual Understanding in 308(1)
Conversation
20.2.3 Experiments and Evaluation 309(4)
20.3 AgentSalon 313(10)
20.3.1 System Overview of Agentsalon 315(2)
20.3.2 Generation of Conversation by 317(1)
Agents
20.3.3 Implementation and Example 318(5)
20.4 Conclusion 323(1)
References 323(2)
21. Chance Discoveries from the WWW
Naohiro Matsumura and Yukio Ohsawa 325(14)
21.1 Introduction 325(1)
21.2 Human Society on the WWW Structure 326(1)
21.3 Direct Relation and Co-citation 327(5)
21.4 Discovering Emerging Topics from the 332(1)
WWW
21.5 Experimental Examples and Discussion 333(4)
21.6 Conclusions 337(1)
References 337(2)
22. Detection of Earthquake Risks with
KeyGraph
Yukio Ohsawa 339(12)
22.1 The Human Society as a Complex System 339(1)
22.2 KeyGraph Applied to Finding Risky 340(1)
Active Faults
22.3 The Seismological Semantics of 341(3)
KeyGraph
22.3.1 The Seismological Semantics of 341(1)
(KG 1)
22.3.2 The Seismological Semantics of 342(2)
(KG2)
22.4 Experimental Support of the 344(5)
Semantics of KeyGraph
22.4.1 Risky Faults and Fundamental 344(1)
Faults
22.4.2 Results of Japanese Risky Faults 344(5)
22.5 Conclusion 349(1)
References 349(2)
23. Application to Questionnaire Analysis
Yumiko Nara and Yukio Ohsawa 351(16)
23.1 The Human Society as a Complex System 351(1)
23.2 A Chance Discovery for Understanding 352(1)
Chance Discovery as an Application of the
Double Helix
23.3 A Double-Helix Process to See the 352(8)
Behaviors of People on the Web
23.4 The Final Revision of the Process 360(1)
Model
23.5 Discussion 361(3)
23.6 Conclusion 364(1)
References 365(2)
24. Chance Discovery for Consumers
Makoto Mizuno 367(16)
24.1 Introduction 367(1)
24.2 Background 368(2)
24.2.1 Empowerment of Consumers 368(1)
24.2.2 Education of Consumers 368(1)
24.2.3 Likelihood vs. Chance Discovery 369(1)
24.3 Proposed Approach 370(6)
24.3.1 Basic Logic of KeyGraph 370(1)
24.3.2 Application of KeyGraph to 371(2)
Expert Knowledge
24.3.3 Clustering Wines and Foods 373(1)
24.3.4 Visualizing Clusters 373(3)
24.4 User Evaluation 376(3)
24.4.1 Constructing a Benchmark 377(1)
24.4.2 Experiment Design 378(1)
24.4.3 Results of the Experiment 378(1)
24.5 Discussion 379(2)
References 381(2)
25. Application to Understanding Consumers'
Latent Desires
Hisashi Fukuda 383(12)
25.1 Latent Desires as Fountains of 383(2)
Chances
25.2 A Method of Chance Discovery: 385(2)
Communications with Data-Based Stimulation
25.3 Application to Discovering Rare but 387(3)
Significant Consumption of Food on
Weekdays or Sunday
25.4 Application to Discovering Rare but 390(5)
Significant Consumption of Food under
Various Weather Conditions
25.5 Conclusion 395(1)
References 395(2)
Author Index 397(1)
Subject Index 398