John S. Gero

ORCID: 0000-0001-9026-535X
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About
Contact & Profiles
Research Areas
  • Design Education and Practice
  • Creativity in Education and Neuroscience
  • Product Development and Customization
  • BIM and Construction Integration
  • Architecture and Computational Design
  • Manufacturing Process and Optimization
  • Color perception and design
  • Semantic Web and Ontologies
  • Team Dynamics and Performance
  • AI-based Problem Solving and Planning
  • Visual and Cognitive Learning Processes
  • Complex Systems and Decision Making
  • Innovative Human-Technology Interaction
  • Spatial Cognition and Navigation
  • Usability and User Interface Design
  • Architecture, Art, Education
  • Multi-Agent Systems and Negotiation
  • Systems Engineering Methodologies and Applications
  • Evolutionary Algorithms and Applications
  • Cognitive Science and Education Research
  • Advanced Multi-Objective Optimization Algorithms
  • Advanced Manufacturing and Logistics Optimization
  • 3D Surveying and Cultural Heritage
  • Interactive and Immersive Displays
  • Data Visualization and Analytics

George Mason University
2013-2024

University of North Carolina at Charlotte
2015-2024

Bridge University
2024

Drexel University
2020-2024

Virginia Tech
2022-2024

Polytechnic University of Turin
2023

University of Waterloo
2023

Luleå University of Technology
2023

Université de Montréal
2022

The University of Sydney
2000-2020

This article begins with an elaboration of models design as a process. It then introduces and describes knowledge representation schema for called prototypes. supports the initiation continuation act designing. Design prototypes are shown to provide suitable framework distinguish routine, innovative, creative design.

10.1609/aimag.v11i4.854 article EN AI Magazine 1990-10-01

10.1016/s0142-694x(96)00023-3 article EN Design Studies 1996-10-01

10.1016/s0142-694x(98)00015-5 article EN Design Studies 1998-10-01

10.1016/s0142-694x(97)00015-x article EN Design Studies 1998-01-01

Abstract In many creative design processes, cross-domain knowledge is required to inspire the new result. Thus, in knowledge-based design, how we represent becomes a key issue. this paper, present formalism for representation. By analyzing function representation different domains, from graphic and industrial architectural engineering device designs, find that although focus of each kind different, can be generalized into small number categories. This used an explorative model by analogy,...

10.1017/s0890060400001633 article EN Artificial intelligence for engineering design analysis and manufacturing 1996-09-01

10.1016/s0950-7051(96)01054-4 article EN Knowledge-Based Systems 1996-11-01

10.1016/s0040-1625(99)00105-5 article EN Technological Forecasting and Social Change 2000-06-01

This paper presents a framework for studying design thinking. Three paradigmatic approaches are described to measure cognitive processes: cognition, physiology and neurocognition. Specific tools methods serve each approach. Design cognition is explored through protocol analysis, black-box experiments, surveys interviews. measured with eye tracking, electrodermal activity, heart rate emotion tracking. neurocognition using electroencephalography, functional near infrared spectroscopy magnetic...

10.1017/dsj.2020.15 article EN cc-by-nc-nd Design Science 2020-01-01

10.1016/s0142-694x(01)00002-3 article EN Design Studies 2001-07-01

Abstract This paper presents how the function–behavior–structure (FBS) ontology can be used to represent processes despite its original focus on representing objects. The FBS provides a uniform framework for classifying processes, and includes higher level semantics in their representation. We show that this supports situated view of based model three interacting worlds. is then describe design processes.

10.1017/s0890060407000340 article EN Artificial intelligence for engineering design analysis and manufacturing 2007-09-19
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