Design

(a subtopic of Reasoning)

AI in Design Webliography. Established in 1994 and maintained by David C. Brown, organizer of the AI in Design Group (AIDG) at Worcester Polytechnic Institute (WPI). "This is a collection of potentially useful sources of information about AI in Design, Knowledge Based Design, Intelligent CAD, Computational Approaches to Design, and Design Theory & Methodology. Included are pointers to projects and research centers (but not to individuals) organized by location. There are also separate sections for announcements, for general information about Engineering Design, for books, for university courses, and for prominent US-based design researchers."

• Also see David C. Brown's 1998 revision of his 1993 article on Intelligent Computer-Aided Design for the Encyclopedia of Computer Science and Technology (Eds.) J.G.Williams & K.Sochats: Intelligent Computer-Aided Design: "[A]lthough design problems in different domains require different domain knowledge -- such as knowledge of equations, components, and analysis techniques -- there are underlying similarities in the form of that knowledge and in the way that it is used. For example, design in any domain requires 'selection'. If we can describe the essential characteristics of this reasoning skill, including the knowledge used and the process, then this can be more easily implemented for any domain [citation omitted]."

"The Design Intelligence Laboratory (DIL) [in the College of Computing at Georgia Institute of Technology] conducts research in knowledge-based reasoning and learning. Design cognition and computing is a focus area for this research, and hence the name 'Design Intelligence.' In the context of design, the main themes of research are teleological models and model-based reasoning & learning, case-based and analogical reasoning & learning, and diagrammatic & visual reasoning. In addition to design, DIL also conducts research on classification, abduction, and reflection. From the perspective of Artificial Intelligence and Machine Learning, DIL develops theories and techniques of knowledge-based reasoning and learning that provide (1) an unified account of memory, reasoning and learning, and (2) an integrated account of the content/representation of knowledge and the processes of reasoning/learning. Results of this research are in the form of architectures and processes for reasoning and learning, and corresponding languages for knowledge representation and organization. Products include knowledge-based systems and tools that embody the methods of reasoning and learning. From the viewpoint of design, DIL develops theories, techniques and tools for enabling (1) conceptual design of physical systems, and (2) functional design of software agents. The goals of the research on conceptual design of physical systems is to develop theories and techniques for creative design, and to build interactive tools for aiding innovation in engineering design. The goals of the research on functional design of software agents is to develop theories and technqiues for self-adaptive software agents, and to build interactive tools for supporting software engineers."

• Be sure see their collection of publications, including this paper by Ashok K. Goel, the lab's Director: Design, Analogy, and Creativity. IEEE Expert: Intelligent Systems and Their Applications 12(3):62-70, May/June 1997. "AI characterizations of creative design tend to be of two kinds. The first begins with a general theory of information processing and then delimits routine, innovative, and creative design in the terms of the theory. ... The second kind of characterization arises from specific AI theories of design. The AI in design theorist posits a computational process for a kind of design activity, and then delimits routine, innovative, and creative design in the terms of the theory." [A preprint of this paper can be accessed from CiteSeer.IST.]

Breathing life into messy sketches. By Nick Easen. CNN (October 13, 2003). "The old computer interface of type, point and click will be replaced by sketch, gesture and talk according to the Artificial Intelligence Laboratory at the Massachusetts Institute of Technology (MIT). 'We have shown that it is credible for a drawing medium to exist that understands what you are sketching, and can then assist with the task in some way,' Randall Davis, whose MIT team are working on the project told CNN. The software observes what we draw on the screen and then turns the sketch into computer code. The smart program is able to interpret what we had in mind from the crude drawing we actually penned out. ... 'People clearly reason spatially and with images as well. We think it will be important for computers to be able to do the same,' says Davis."

• Visit the the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) for an overview of Sketch Understanding and Design Rationale Capture, the related projects, a collection of publications, and much more.

Design and Assumption-Based Reasoning. Slides for Lecture #1 covering Section 1 of Chapter 9 [Assumption-Based Reasoning] inComputational Intelligence: A Logical Approach, David Poole, Alan Mackworth and Randy Goebel. (Oxford University Press, New York; 1998). As per slide one: "Often we want our agents to make assumptions rather than doing deduction from their knowledge. For example . . . In design you hypothesize components that provably fulfill some design goals and are feasible."

Key Centre of Design Computing and Cognition at the University of Sydney.

Optimal Design Laboratory at the University of Michigan. "In recent years our research has focused on the design of large scale complex systems, including decomposition and coordination, model approximations, product families, and integrated artifact design and control. We are also studying the links between engineering design and industrial design, art, conceptual design, artificial intelligence, finance, organizational design, and psychology."

Related AI Topics Pages

• Architecture & Design
• Engineering
• Interfaces
• Reasoning