
Design Computing and Cognition'24
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This book publishes the reviewed and revised texts of the papers delivered at the Tenth International Conference on Design Computing - DCC'24 held at Concordia University in Montreal, Canada. These papers the range of design research from artificial intelligence, cognitive science, cognitive neuroscience and computational theories applies to design.
The papers are published in two volumes and are grouped under the following headings: Design Processes, Design Creativity, Design Cognition, Shape and Form, Design Technology, AI and Design, Design and Brain Behaviors, and Design AI Applications. These two volumes form an archival record of then current cutting-edge research studying design scientifically. They demonstrate the range of approaches being used to characterize designing as a process. At the same time they show that there is a commonality in designing independent of design discipline. These volumes will be of interest to design researchers in both academia and industry and to anyone who needs to obtain a better understanding of designing.
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John Gero has been a Professor of Design Science, Architecture, Artificial Intelligence, Civil Engineering, Cognitive Psychology, Computer Science, Design and Computation and Mechanical Engineering in Australia, France, Switzerland, UK and USA. He is the Chair of the international conference series Design Computing and Cognition.
Content
- Intro
- Preface
- List of Reviewers
- Contents
- I Design Technology
- How Do We Know if a Design Space Representation Is Useful? Insights from the DS-Viz Method
- 1 Introduction
- 1.1 Computational Approaches to Design Space Representation
- 1.2 Creativity-Focussed Approaches to Design Space Representations
- 2 DS-Viz: Creating Design Space Visualisations
- 3 Strategies for the Evaluation of DS-Viz Usefulness
- 3.1 Brief Notes on Steps A and B
- 3.2 Evaluation of Step C: Distance Measure
- 3.3 Evaluation of Step D: Embedding Creation
- 3.4 Evaluation of Step E: Creation of Space Visualisations
- 3.5 Evaluation of Step F: Relevant Metrics
- 4 Discussion
- 5 Conclusions
- References
- Visual Attention to Biophilic Elements in Virtual Classroom Design: A VR Eye-Tracking Study
- 1 Background
- 2 Aims
- 3 Experiment Methods
- 3.1 Participants
- 3.2 Virtual Classroom Simulation
- 3.3 Hardware and Software
- 3.4 Experiment Procedure
- 3.5 Data Collection and Processing
- 3.6 Data Analysis Methods
- 4 Data Analysis and Results
- 4.1 Heatmaps and Distribution of AOI
- 4.2 Statistical Analysis of Fixations and Saccades
- 5 Discussions
- 6 Limitations and Directions for Future Research
- 7 Conclusion
- References
- Optimization in Parametric Design Thinking: Are New Models Needed?
- 1 Introduction
- 2 Background
- 2.1 Models of Design Thinking
- 2.2 Models of Parametric Design Thinking
- 2.3 Optimization in Architectural Design
- 3 Method
- 3.1 Criteria for a Parametric Design as a Novel Process
- 3.2 Optimization Strategies
- 4 Results
- 4.1 Overview of the Iterative Cycles from Our Study
- 4.2 Comparing Optimization Behaviors Against Criteria for New Forms of Design Thinking
- 5 Discussion: A Proposal for Future Updates
- 5.1 Limitations
- 6 Conclusion
- References
- Symmetry Heuristics for Stable Reinforcement Learning Design Agents
- 1 Introduction
- 2 Background
- 2.1 The Synergy Between Configuration Design and Graph-Based Frameworks
- 2.2 Configuration Design with Graph Neural Network Based Reinforcement Learning Agents
- 2.3 Heuristic-Guided Reinforcement Learning
- 3 Methods
- 3.1 Foundational RL Framework for Configuration Design
- 3.2 Symmetry Reward Formulation
- 4 Case Study
- 4.1 Truss Design Problem
- 4.2 Training and Evaluating the RL Agents
- 5 Results and Discussion
- 6 Conclusion and Future Work
- References
- II AI and Design
- Using Personas to Increase the Diversity of Design Concepts Generated by Large Language Models
- 1 Introduction
- 2 Background
- 2.1 Design Ideation
- 2.2 Large Language Models (LLMs)
- 2.3 Design Representations
- 3 Methods
- 3.1 Knowledge Base Construction
- 3.2 Design Problems and Professional Persona Selection
- 3.3 Prompt Design and Evolution
- 4 Experiment with Design Problems
- 4.1 Evaluation Metrics
- 4.2 Sample Outputs from ChatGPT
- 4.3 Results and Discussion
- 5 Conclusion
- References
- A Multi-case Study of Traditional, Parametric, and Generative Design Thinking of Engineering Students
- 1 Background and Motivation
- 2 Methods
- 2.1 Participants
- 2.2 Curriculum Materials
- 2.3 Aladdin and Design Process Behavior
- 2.4 Data Collection
- 2.5 Data Analysis Approach
- 3 Results and Discussion
- 3.1 Case Study 1: Sabrina (P102)
- 3.2 Case Study 2: Suyash (P103)
- 3.3 Case Study 3: Ricky (P108)
- 3.4 Cross-Case Analysis and Discussion
- 3.5 Quantitative Results and Discussion
- 4 Limitations and Future Work
- 5 Conclusion
- References
- MazeMind: Exploring the Effects of Hand Gestures and Eye Gazing on Cognitive Load and Task Efficiency in an Augmented Reality Environment
- 1 Background/Motivation
- 2 Objectives and Significance
- 3 Materials and Methods
- 3.1 Hypotheses
- 3.2 Laboratory Experiments
- 4 Results and Analysis
- 4.1 Participants
- 4.2 Statistical Results and Analysis
- 4.3 Hypothesis Analysis
- 5 Discussion
- References
- What Is Generative in Generative Artificial Intelligence? A Design-Based Perspective
- 1 Introduction: The Generative Trend in Artificial Intelligence
- 2 Background and Scope: Generativity and Artificial Intelligence, Two Disciplines that Can Enlighten One Another
- 2.1 Generative Artificial Intelligence: Definition and Models
- 2.2 Generativity: What Can Engineering Design Bring to Artificial Intelligence
- 2.3 Characterizing Generativity in Artificial Intelligence?
- 3 Method: A Canonical Model of Generativity to Analyze Systematically GenAI Models
- 3.1 A Theoretical Framework of Generativity
- 3.2 A Systematic Analysis of GenAI Models
- 4 Artificial Intelligence Models Within This Framework
- 4.1 Objective and Scope of the Section
- 4.2 Generativity in Generative Design Algorithms (GDA)
- 4.3 Generativity in Variational Auto-Encoders (VAE)
- 4.4 Generativity in Generative Diffusion Models (GDM)
- 4.5 Generativity in Generative Adversarial Networks (GAN)
- 4.6 Generativity in Generative Pre-Trained Transformers (GPT)
- 5 Results: An Overview of Generativity on Generative AI
- 6 Discussions
- 6.1 Confirming the Generative Power of GenAI
- 6.2 Identifying Generative Mechanisms as an Open Debate for GenAI
- 6.3 Confirming Some Limits of GenAI Tools
- 6.4 Avenues to Enrich the Generative Power of GenAI Tools
- 7 Conclusion
- References
- Design and Brain Behaviors
- An Exploration of Brain Lateralization During Engineering Design Ideation Using fNIRS
- 1 Introduction
- 2 Background
- 3 Research Questions
- 4 Methodology
- 4.1 Data Collection and Processing
- 4.2 Data Analysis
- 5 Results
- 6 Discussion
- 7 Conclusion
- References
- A New Approach for Examining the Changes of Brain Behaviors Between Problem-Finding and Problem-Solving in Design Teamwork
- 1 Introduction
- 1.1 Neuroscience in Design Teamwork
- 2 Methodology
- 2.1 Pilot Study
- 2.2 Inter-Brain Synchrony Data Analysis
- 3 Discussion and Limitation
- 4 Conclusion
- References
- Higher-Order Cognitive States Recognition in Open-Ended Design Creation Tasks Using EEG Microstate Analysis and Convolutional Neural Networks
- 1 Introduction
- 2 Methods
- 2.1 Dataset
- 2.2 Experiment Design
- 2.3 Participants and Procedure
- 2.4 Reconstruction of EEG Microstates Sequences
- 2.5 Classification of EEG Microstate Sequences and Network Configuration
- 2.6 Network Configuration
- 3 Results
- 4 Discussion
- 5 Conclusion
- References
- Neuro-Cognitive Feedback as a Tool for Improving Cognitive Endurance in Engineering Design
- 1 Introduction
- 2 Background
- 2.1 Neuro-Cognitive Feedback to Enhance Engineering Design Ideation
- 2.2 How Does Neuro-Cognitive Feedback Enhance Ideation?
- 2.3 Why Functional Near-Infrared Spectroscopy?
- 3 Research Questions
- 4 Methods
- 4.1 Neuro-Cognitive Feedback Display
- 4.2 Ideation Task
- 4.3 Data Analysis
- 5 Results
- 6 Discussion
- 7 Conclusion
- References
- Comparing Engineering Designers' Brain Activity in Visuospatial Reasoning Tasks
- 1 Introduction
- 2 Background and Related Work
- 2.1 Models of Visuospatial Reasoning in (Engineering) Design
- 2.2 Assessing Visuospatial Reasoning in Engineering Design
- 2.3 EEG Studies of Visuospatial Reasoning Tasks
- 3 Research Methodology
- 3.1 Visuospatial Reasoning Tasks
- 3.2 Experimental Procedure
- 3.3 Experimental Setup
- 3.4 EEG Data Pre-Processing
- 3.5 Data Analysis
- 4 Results
- 4.1 Developments vs. Rotations
- 4.2 Developments vs. Views
- 4.3 Rotations vs. Views
- 5 Discussion
- 6 Conclusions and Further Work
- References
- DESIGN AI APPLICATIONS
- Assessing the Alignment Between Word Representations in the Brain and Large Language Models
- 1 Introduction
- 2 Background
- 2.1 Modeling Neural Representations of Language With Language Models
- 2.2 Applying Representational Similarity Analysis (RSA) to Compare Representations of Language
- 2.3 Cognitive Processes Underlying Creativity and Design
- 3 Methods
- 3.1 Word Association Task
- 3.2 fMRI Data Analysis
- 3.3 Language Model: Llama-2 7b
- 3.4 RSA Analysis
- 4 Results and Discussion
- 4.1 Assessing Brain-LLM Alignment Across Participants and LLM Layers
- 4.2 Comparing Brain-LLM Alignment Between Task Conditions
- 4.3 Implications for Design
- 5 Limitations and Future Work
- 6 Conclusion
- References
- Architectural Creativity Stranded at Mid Journey? Evaluating Creative Potential of Prompts and Images in Generative AI
- 1 Generative Artificial Intelligence in the Design Process
- 2 Intelligence and Creativity: Intersecting or Disjointed Concepts?
- 3 Computable Approaches to Design and the Evolution of Generative AI
- 4 The Use of Mid-Journey Diffusion Model in Architectural Design: A Case Study
- 5 Design Verbalization
- 6 Methodology
- 7 GAI Images and Prompts Considered Most and Least Creative
- 8 Prompt Structure and Their Characteristics: Implications for Design Verbalization
- 9 Creative Descriptors of Design Experts and AI
- 10 Conclusions
- References
- Semantic Properties of Word Prompts Shape Design Outcomes: Understanding the Influence of Semantic Richness and Similarity
- 1 Introduction
- 2 Related Work
- 2.1 The Use of Semantic and Visual Representations in Design
- 2.2 Semantic Properties and Their Impact on Cognition and Creativity
- 3 Experimental Design
- 3.1 Participants
- 3.2 Design Task and Interface
- 3.3 Semantic Richness and Similarity of Prompt Words
- 4 Design Outcome Characterization and Modeling Procedure
- 4.1 Relationship with Semantic Richness
- 4.2 Relationship with Semantic Similarity
- 5 Results
- 5.1 The Relationship Between Outcomes and Semantic Richness Dimensions
- 5.2 Semantic Richness Does Not Fully Account for Outcome Variation
- 5.3 The Relationship Between Semantic Similarity and Outcome Distinctiveness
- 6 Discussion
- 6.1 Types of Design Outcomes Can Predict Semantic Richness Dimensions
- 6.2 Increasingly Similar Word Prompts Are Associated with Harder-To-Distinguish Design Outcomes
- 6.3 Implications for Design and Future Directions
- 6.4 Limitations
- 7 Conclusion
- References
- Overcoming Design Challenges in Coupled System Device Problems Using Agent-Based Models
- 1 Introduction
- 2 Background
- 2.1 Agent-Based Modeling and Multi-Agent Systems
- 2.2 Agent-Based Models in Engineering
- 2.3 Design Variables in Agent-Based Models
- 3 Methodology
- 3.1 Firefighting Drones Case Study
- 3.2 Modeling and Analysis
- 4 Results
- 5 Discussion
- 6 Conclusion
- References
- Correction to: How Do We Know if a Design Space Representation Is Useful? Insights from the DS-Viz Method
- Correction to: Chapter 1 in: J. S. Gero (Ed.): Design Computing and Cognition'24, https://doi.org/10.1007/978-3-031-71922-6_1
- Author Index
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