Design Computing and Cognition '10 (eBook)

John Gero is a Research Professor at the Krasnow Institute for Advanced Study and at the Volgenau School of Information Technology and Engineering, George Mason University. Formerly he was Professor of Design Science and Co-Director of the Key Centre of Design Computing and Cognition, at the University of Sydney. He is the author or editor of 48 books and over 600 papers and book chapters in the fields of design science, design computing, artificial intelligence, computer-aided design, design cognition and cognitive science. He has been a Visiting Professor of Architecture, Civil Engineering, Cognitive Science, Computer Science, Design and Computation or Mechanical Engineering at MIT, UC-Berkeley, UCLA, Columbia and CMU in the USA, at Strathclyde and Loughborough in the UK, at INSA-Lyon and Provence in France and at EPFL-Lausanne in Switzerland. His former doctoral students are professors in the USA, UK, Australia, India, Japan, Korea, Singapore and Taiwan.He has been the recipient of many excellence awards including the Harkness Fellowship, two Fulbright Fellowships, two SRC Fellowships and various named chairs. He is on the editorial boards of numerous journals related to design science, computer-aided design, artificial intelligence and knowledge engineering and is the chair of the international conference series Artificial Intelligence in Design, the new conference series Design Computing and Cognition and the international conference series Computational and Cognitive Models of Creative Design.Professor Gero has published books with Springer, Kluwer, North-Holland, Addison-Wesley, and Academic Press amongst others.

Preface 5
Contents 7
List of Reviewers 12
Part I: Design Cognition 15
A Comparison of Cognitive Heuristics Use between Engineers and Industrial Designers 16
Introduction 16
Design Heuristics 17
Experimental Approach and Research Questions 18
Participants 19
Method 19
Results 21
Types of Concepts Generated 21
Evidence of Heuristic Use 23
Characterizing Design across Sessions 28
Design Heuristics and Concept Diversity, Creativity, and Practicality 32
Discussion 33
Conclusions 34
References 34
Studying the Unthinkable Designer: Designing in the Absence of Sight 36
Aim 36
Significance 37
Designer and Method 39
Unthinkable Designs 39
Findings 41
Discussion 43
Conclusions 45
References 46
Design Heuristics: Cognitive Strategies for Creativity in Idea Generation 48
Introduction 48
Designers’ Cognitive Processes 49
Design Heuristics 50
Experimental Approach and Research Questions 53
Participants 53
Materials 54
The Design Task 54
Design Heuristics Instructional Materials 55
Experimental Design 56
Procedure 57
Results 57
Average Creativity Ratings of Selected Designs 59
Heuristic Use 60
Discussion 63
References 65
An Anthropo-Based Standpoint on Mediating Objects: Evolution and Extension of Industrial Design Practices 67
Introduction - A Shift in Design Tools’ Consideration 67
Rationale of the Study: Understanding the Use of Design Tools through a Three Phases Proposition 69
First Phase: Addressing the Question from an “Anthropo-Based” Standpoint 70
Second Phase: Focusing on “Mediating Objects” 71
Third Phase: Undoing the Comparative/Dichotomous Approach to the Benefit of the Study of Complementarities 72
Method 73
Twelve Conversations to List the Context Factors: An Exploratory Research 73
Detailed Research 77
Results 81
Testing the Complementarities 81
Going Further in the Analysis of Mediating Objects 83
Conclusions - Toward Augmented Design Tools Closer to Real Practices 85
References 85
Part II: Framework Models in Design 87
Beyond the Design Perspective of Gero's FBS Framework 88
Introduction 88
Related Work 89
Extended Model 93
Using and Interacting: The Interacting Interface 97
Knowledge, Affordances and the Interacting Interface 99
Failure and Misuse 101
Exemplary Application of the Proposed Model and Discussion 102
Conclusions 105
References 106
A Formal Model of Computer-Aided Visual Design 108
Introduction 108
Classifications and Logics for a Design Model 110
A Formal Model 112
A Design Task Domain 112
A Computer Visualization Domain 113
A Physical Design Actions Domain 119
The Active Perception 120
The Operation Method 121
The System of Computer-Aided Visual Design 121
Conclusions 123
References 123
Design Agents and the Need for High-Dimensional Perception 125
Introduction 125
Relevance 127
Example Systems: Observing Types in Architecture 128
Method and Results 131
Effect of Overall Attribute Dimensionality on Classification 132
Effect of Particular Sets of Attribute Dimensions 135
Different Sets of Attributes for Different Classes 137
Mutual Classification and Communication 139
Conclusion 140
References 143
A Framework for Constructive Design Rationale 145
Introduction 145
An Ontological Representation of Design Rationale 146
The FBS Ontology 146
Instance-Based and State-Space Views of Design Rationale 147
An FBS View of Design Rationale 149
What Is Constructive Design Rationale? 151
Drivers of Constructive Design Rationale 154
The Situated FBS Framework of Designing 154
Drivers for Constructing Rationale Structure 156
Drivers for Constructing Rationale Behaviour 159
Drivers for Constructing Rationale Function 160
Conclusion 161
References 162
Part III: Design Creativity 164
The Curse of Creativity 165
Introduction 165
Theoretical and Perceived Creativity 166
Current Approaches 167
Some Research Alternatives 168
New Wine in Old Bottles 168
Using Cognitive Science and Psychology 169
Products as Art 170
Ingredients of Routine Design Reasoning 170
Modifications to Routine Design Reasoning 173
Assumptions and Restrictions 173
Matching Creative and Routine Reasoning 173
Basic Synthesis & Criticism: Possible Modifications
Summary & Conclusions
References 177
Enabling Creativity through Innovation Challenges: The Case of Interactive Lightning 179
Introduction 179
The Indianapolis Tunnel Scenario 180
Cellular Automata Models 183
Asynchronous Cellular Automata 183
Dissipative Cellular Automata 183
Cellular Automata with Memory 184
Adaptive Interactive Lightning Model 184
The Design Environment 188
The Cells Simulator 189
The Lights View 190
The System Configuration 190
Experimenting Different Configurations 191
From a Prototype to a Product 192
Conclusions and Future Work 194
References 195
Facetwise Study of Modelling Activities in the Algorithm for Inventive Problem Solving ARIZ and Evolutionary Algorithms 196
Conceptual Design Techniques Are Non Quantitative 196
Aims 197
Conceptual and Detailed Design Stages Do Not Speak the Same Language 197
Translation from One Language to the Other Is Required 198
Significance 198
Approach Followed in the Paper 198
Notions and Definitions 199
Hilbert Space: Search Strategy Driven by Objectives 199
Steady State: Facets of the Notion in Various Scientific Fields 199
C-K: Distinction between Concepts and Knowledge 200
Evolutionary Algorithms (EA) 200
Algorithm for Inventive Problem Solving (ARIZ) 200
Modelling Requirements in Evolutionary Algorithms 201
Inventive Problem Solving through Evolutionary Algorithm Is Restricted by the Lack of Dynamism in Models 201
Design Space Limits in Model Segmentation Approach 201
Practical Limit of Model Segmentation 202
Modelling Requirements in ARIZ 203
Facetwise Modelling Reduces Complexity 203
Facetwise Modelling Reduces Complexity 205
Modelling Activities When Evaluating Logical Status of a Proposition 205
Conflicting Requirements of Two Steady States 206
Steady States Discontinuities and Integrated Framework 207
Structuring Design Space through Multiple Contradictions Statements 209
Interconnecting Parsimonious Models Thanks to Systemic Approach 209
A Model Structuring That Enables Travelling through Design Space 209
Discussion 210
Spatial Segmentation of Models vs. Propagation of Contradictions through System Levels 210
Conclusion 211
Expected Outcomes from Proposed Modelling Description 212
References 212
Exploring Multiple Solutions and Multiple Analogies to Support Innovative Design 215
Introduction 216
Background 217
Analogical Reasoning as Basis for Innovative Design 217
Cognitive Models of Analogical Reasoning 218
Structure Mapping Theory and Structural Alignment 218
One-to-One Mapping Constraint 220
Research Questions and Hypotheses 220
Experiment 1- Generating Multiple Solutions 221
Overview 221
Method 222
Metrics 224
Results and Discussion 225
Experiment 2- Learning Design Principles from Multiple Analogs 225
Overview 225
Method 226
Metrics 228
Results and Discussion 229
Conclusion 230
Future Work 231
References 231
Creative and Inventive Design Support System: Systematic Approach and Evaluation Using Quality Engineering 234
Introduction 234
Systematic Approach for the Creative and Inventive Thinking Process 237
Proposal of the Thinking Process 237
Problem Understanding Process 239
Problem Solving Process 240
Quantitative Evaluation for the Creative and Inventive Thinking Process of CDSS 246
Evaluation for the Problem Understanding 246
Validation of the Problem Solving Using the Design of Experiment 247
Evaluation of the Robustness Using the Taguchi Method 250
The Taguchi Method 250
Solving Phase with Robustness 251
Conclusion 252
References 252
Part IV: Line, Plane, Shape, Space in Design 254
Line and Plane to Solid: Analyzing Their Use in Design Practice through Shape Rules 255
Introduction 255
Aims and Significance 257
Method 258
Participants, Protocol Locations, and the Design Task 258
Deriving Shape Rules from Segmentation Schemes 259
Results 262
Parallel Computations across Design Descriptions 263
Spatial Relations between Design Descriptions 265
Medial Lines as Correlative Devices 267
Discussion and Conclusions 269
References 271
Interactions between Brand Identity and Shape Rules 272
Introduction 272
Background 273
Product Shape Design 273
Brand Identity 274
Shape Grammars and Shape Rules 275
The Role of a Computer Aided Design Synthesis System 276
Method 277
Results 278
Selection of the Corpus of Designs 279
Derivation of Brand Characteristics 280
Definition of a Shape Grammar 282
Generation of New Designs 284
Evaluation of the New Designs 285
Conclusion 285
References 286
Approximate Enclosed Space Using Virtual Agent 288
Enclosed Space 288
Previous Simulation on the Interaction between Architectural Design and Virtual Agent 289
Geometric Modeling of Enclosed Space 290
Co- linearity of Three Points 291
The Determination of Convex Area 292
Define Centroid 292
Method 293
Acquiring Information of the Environment 294
Decomposing Object into Set of Points 295
Determining Convex Area and Centroid 296
Experiment 298
Constructing Agent and Environment 299
Computational Process and Result 301
Concluding Remarks and Future Development 305
References 305
Associative Spatial Networks in Architectural Design: Artificial Cognition of Space Using Neural Networks with Spectral Graph Theory 307
Introduction 307
Artificial Neural Networks in Architectural Systems 308
Overview of the Design Problem and Approach 309
Generation of Spatial Graphs for the Exhibitions 310
Mapping of Exhibits Using Dimensionality Reduction 310
Spectrum Representation of Graph Features for Synaptic Vectors 312
Mapping a Modified Growing Neural Network 313
Clustering of Graph Types Using a Growing Neural Gas 314
Results 315
Investigation of Dynamic Inputs 315
Generation of Spatial Layouts – ‘An Artificial Curator’ 317
Spatial Realisation of Graphs Using a Particle Repulsion Algorithm 319
Meta-cognition of Exhibition Spaces by Users 320
Integrating Participant Users Using Unsupervised Goals 320
Conclusion 322
References 324
Part V: Decision-Making Processes in Design 326
Comparing Stochastic Design Decision Belief Models: Pointwise versus Interval Probabilities 327
Introduction 327
Stochastic Representations 329
Functional Distribution Representation 330
Functional Representation of Interval Probabilities 331
Agent Learning 333
PDF Updating 333
Credal Set Updating 334
Agent Implementation 334
UCI Car Design Database 335
Empirical Trials 337
Results 339
Discussion 343
Conclusion 343
References 344
A Redefinition of the Paradox of Choice 346
Introduction 346
The Paradox of Choice and Mass Confusion 348
The Paradox of Choice 348
Wundt Curve Representation of the Paradox of Choice 349
Mass Customization and Mass Confusion 350
Means of Solving Problems Related to the Paradox of Choice and Mass Confusion 351
Limitation of Solution Space According to Averaged Users’ Needs 352
Nourishing Online Communities 352
Learning from the Users with the Aid of Recommender Systems 352
Redefinition of the Paradox of Choice 353
Lack of Meaningful Choice 354
Inability to Express What a Meaningful Choice Is 354
Multidimensional Wundt Curve Model of the Paradox of Choice 355
Solution to the Paradox of Choice 356
Computational Approach to the Paradox of Choice 356
Learning from the Users with an Aid of Recommender Systems 356
Recognition of Meaningful Options with the Aid of Evolutionary Algorithms 357
Experiment Setup 358
Types of Configurators Subject to Experiment 359
Experimental Method 360
Findings 361
Conclusion 362
References 363
Rethinking Automated Layout Design: Developing a Creative Evolutionary Design Method for the Layout Problems in Architecture and Urban Design 366
Introduction 366
Between Intuition and Rationality 367
Revisiting Automated Layout Design 369
Constraint-Based Systems 369
Cellular Automata and Agent-Based Systems 370
Shape Grammars 371
Physically-Based Systems 372
Evolutionary Algorithms 373
Conclusion 374
Developing an “Adaptive” Layout-Design System 375
Definition of Layout 376
Generative Mechanism 378
Interaction with the Situation 381
Conclusion / Outlook 383
References 383
Applying Clustering Techniques to Retrieve Housing Units from a Repository 386
Introduction 386
BARCODE HOUSING SYSTEM: A Generative System for Housing Design 387
Housing Layout Workspace 389
Housing Selection Workspace 390
Clustering Housing Layouts 393
Clustering Algorithms 395
Results 396
Conclusions 398
References 399
Part VI: Knowledge and Learning in Design 401
Different Function Breakdowns for One Existing Product: Experimental Results 402
Introduction 402
Research Design 404
Results of the Experiment: Representations, Notions and Approaches 406
Typical Representation 406
Different Notions and Expressions of Function 408
Diverging Terminology for Parts 410
Different Approaches 411
Results: Different Functional Breakdowns 411
The Function Trees 411
A Comprehensive Model of the Pump 412
Similarities in the Layout of the Function Trees 416
Mistakes in the Trees 418
Conclusions and Implications 419
References 420
A General Knowledge-Based Framework for Conceptual Design of Multi-disciplinary Systems 422
Introduction 422
Representing Desired Functions 423
The Representation of Flows 424
The Representation of Constraints on Flows 426
The Representation of Desired Functions 426
Representing the Functional Knowledge of Known PSs 427
The Input-Output Flow Name Pair 428
The Constraints on Input Flows 428
The Constraints on Output Flows 429
The Attribute-Mapping Rules 429
The General Functional Knowledge Representation Schema 430
An Agent-Based Design Synthesis Approach 431
The Agent-Based Design Synthesis Process 431
An Illustrative Case 433
Discussion 437
Conclusions 439
References 439
Learning Concepts and Language for a Baby Designer 441
Symbols and Design Reasoning 441
Learning Symbols 443
Are Designs Emergent? 444
Related Work: Discovering Patterns in Design Spaces 445
Baby Designer Enterprise 446
Learning Containment 446
Learning about Clearance 448
Language Mapping 450
Associating Linguistic Labels 451
Discussion 456
Conclusion 456
References 458
Organizing a Design Space of Disparate Component Topologies 460
Introduction 460
Background 461
Generation of Designs 463
Reducing the Design Space through Confluence and Matrix Comparison 465
Organizing the Design Space Using Clustering Methods 467
Results and Discussion 471
Conclusion 477
References 478
Part VII: Using Design Cognition 481
Imaging the Designing Brain: A Neurocognitive Exploration of Design Thinking 482
Introduction 482
Research Questions and Objectives 484
Methods 485
Experimental Set-Up and Tasks 485
Procedures 488
Results 489
Semi-Structured Interviews 489
Behavioral Analysis 490
fMRI Analysis 490
Discussion and Conclusions 494
References 496
A Computational Design System with Cognitive Features Based on Multi-objective Evolutionary Search with Fuzzy Information Processing 498
Introduction 498
Evaluating Design Performance 500
Multi-objective Evolutionary Search with a Relaxed Dominance Concept 504
Multi-objective Evolutionary Algorithm & Fuzzy Neural Tree = A Computational Design System with Cognitive Features
The Cognitive Features of the System 508
Application 511
Conclusion 516
References 516
Narrative Bridging 518
Introduction 518
The Key Elements of Narrative Bridging 520
Media 520
Premise 521
Goal 521
Syuzhet 521
The Process of Using Narrative Bridging 522
Phase 1 522
Phase 2 523
Phase 3 524
Prototype Testing of the Method 526
The Masquerade Game 527
The Parasite Game 530
Results 532
Conclusions and Further Work 535
References 536
Generic Non-technical Procedures in Design Problem Solving: Is There Any Benefit to the Clarification of Task Requirements? 538
Introduction 538
Aims 540
Method 540
Independent Variable, Experimental Design and Procedure 540
Dependent Variables and Instruments 541
Results 542
Check of Randomization of the Intervention Groups 542
Performance Benefit of the Additional Offer of the GQAS 542
Reported Benefit of the Additional Offer of the GQAS 542
Conclusions 545
References 546
Virtual Impression Networks for Capturing Deep Impressions 552
Introduction 552
Surface and Deep Impressions 553
Preferences and Deep Impressions 553
Viewpoint of This Study 554
Structure of Impressions 554
Inexplicit Impressions 554
Purpose and Method 555
Virtual Impression Network 555
Semantic Network 556
Structure Analysis 557
Experiment 559
Method 559
Results 560
Analysis 560
Preprocess 561
Analysis of the Structure of the Virtual Impression Network 562
Comparison with the Preliminary Experiment 568
Conclusion 569
References 570
Part VIII: Collaborative/Collective Design 572
Scaling Up: From Individual Design to Collaborative Design to Collective Design 573
Introduction 573
A Conceptual Space for Collective Design 575
The Representation Dimension 578
The Communication Dimension 580
The Motivation Dimension 581
Mapping Collective Intelligence to the Conceptual Space for Collective Design 583
Principles for Collective Design 587
Conclusions 590
References 590
Building Better Design Teams: Enhancing Group Affinity to Aid Collaborative Design 592
Introduction 592
Background and Context 594
The ConvoCons Approach 595
ConvoCons System Architecture 596
Methods 597
Framework for Measuring Affinity 599
Coding for Affinity 600
Results 602
Exit Survey 602
Completion Time – Log Data 603
Quantitative Evaluation of Video Data 603
Exit Interviews 606
Discussion 607
Conclusion 608
References 609
Measuring Cognitive Design Activity Changes during an Industry Team Brainstorming Session 612
Introduction 612
Quantifying Design Processes 612
The FBS Ontology 613
The Brainstorming Session 614
Qualitative Observations 615
Quantitative Observations 615
FBS Segmenting and Coding 617
Results 617
Producing Design Processes from a Linkograph 622
Team Design Processes 623
Comparing Design Process Distributions 624
Interactions between Team Members Measured through Processes 626
Changes in Interaction during Design Session 629
Conclusion 630
References 630
Part IX: Design Generation 632
Interactive, Visual 3D Spatial Grammars 633
Introduction 633
Background 635
Formalism 635
Grammar Interpreters 635
Existing Spatial Grammar Implementations 636
Challenges 637
Approach 638
Development of Spatial Grammar Rules 639
Application of Spatial Grammar Rules 643
Implementation 645
Examples 646
Discussion 649
Conclusion 650
References 651
A Graph Grammar Based Scheme for Generating and Evaluating Planar Mechanisms 653
Introduction – An Overview 653
Background 655
Graph Representation 656
Rules for Design Generation 659
Evaluation 661
Results: Rule Validity 663
Discussion 665
Conclusion 667
References 668
A Case Study of Script-Based Techniques in Urban Planning 670
Introduction 670
Significance 671
Overview 672
Computational Tools 672
Application of Computational Tools at Urban Scale 674
Application of Computational Tools at Building Scale 680
Low-Rise typology 680
Tower 682
Conclusion and Future Work 687
References 688
Complex Product Form Generation in Industrial Design: A Bookshelf Based on Voronoi Diagrams 690
Introduction 690
Background 692
Expanding the Morphologic Repertoire in Design 692
Form Generation in the Larger Context 692
Related Works on Generative Product Design Systems 693
Approach 694
Short on the Voronoi Structure 695
The Bookshelf 695
User Interaction 696
The Interface 697
Interface Evaluation 698
The General Search Algorithm 700
The GA Characteristics 702
Constraints, Objectives and Evaluation 703
Results 704
Conclusion and Further Research 707
References 707
A Computational Concept Generation Technique for Biologically-Inspired, Engineering Design 710
Introduction 710
Related Work 712
Supporting Design Tools 713
Functional Basis Design Language 713
Concept Generation Software-MEMIC and Design Repository 714
Organized Search Tool 714
Engineering-to-Biology Thesaurus 716
Computational Concept Generation Technique 717
Algorithm 718
Concept Generation Example 719
Smart Flooring 719
Discussion 723
Conclusions 725
References 726
First Author Email Address 730
Author Index 732