Exploration and innovation in design: towards a computational model
Exploration and innovation in design: towards a computational model
Introduction: a new agenda for computer-aided design
The electronic design studio
Using aggregation and dynamic queries for exploring large data sets
CHI '94 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
TileBars: visualization of term distribution information in full text information access
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
The Influence Explorer (video)—a tool for design
Conference Companion on Human Factors in Computing Systems
CHI '94 Conference Companion on Human Factors in Computing Systems
Design perspectives in visualising complex information
Proceedings of the third IFIP WG2.6 working conference on Visual database systems 3 (VDB-3)
Creating an Accurate Portrayal of Concurrent Executions
IEEE Concurrency
Dynamic Aggregation with Circular Visual Designs
INFOVIS '98 Proceedings of the 1998 IEEE Symposium on Information Visualization
Automatic abstraction management in information visualization systems
IV '97 Proceedings of the IEEE Conference on Information Visualisation
Special Issue: Configuration Design
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Moving Happily through the World Wide Web
IEEE Computer Graphics and Applications
Designing effective program visualization tools for reducing user's cognitive effort
Proceedings of the 2003 ACM symposium on Software visualization
SoftVis '05 Proceedings of the 2005 ACM symposium on Software visualization
GSPIM: graphical visualization tool for MIPS assembly programming and simulation
Proceedings of the 37th SIGCSE technical symposium on Computer science education
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COMIND is a tool for conceptual design of industrial products. It helps designers define and evaluate the initial design space by using search algorithms to generate sets of feasible solutions. Two algorithm visualization techniques, Kaleidoscope and Lattice, and one visualization of n-dimensional data, MAP, are used to externalize the machine's problem solving strategies and the tradeoffs as a result of using these strategies. After a short training period, users are able to discover tactics to explore design space effectively, evaluate new design solutions, and learn important relationships among design criteria, search speed, and solution quality. We thus propose that visualization can serve as a tool for interactive intelligence, i.e., human-machine collaboration for solving complex problems.