Content:
- Reich, Y. (1994), “Guest editorial:
Special issue: research methodology,” Artificial Intelligence for Engineering
Design, Analysis, and Manufacturing, vol. 8, no. 4, pp. 261-262.
- Reich, Y. (1994), “Layered models of research
methodologies,” Artificial Intelligence for Engineering Design,
Analysis, and Manufacturing, vol. 8, no. 4, pp. 263-274.
Abstract: The status of research methodology employed by studies on
the application of AI techniques to solving problems in engineering
design, analysis, and manufacturing is poor. There may be many reasons for
this status including: unfortunate heritage from AI, poor educational
system, and researchers' sloppiness. Understanding this status is a
prerequisite for improvement. The study of research methodology can
promote such understanding, but most importantly, it can assist in
improving the situation. This paper introduces concepts from the
philosophy of science and builds on them models of worldviews of science.
These worldviews are combined with a research heuristics or research
perspectives and criteria for evaluating research to create a layered
model of research methodology. This layerd model
can serve to organize and facilitate a better understanding of future
studies of research methodologies. The paper discusses many of the issues
involved in the study of AI and AIEDAM research methodology using this
layered model.
- Dym,
L. C. and Levitt, R. E. (1994), On the
Evolution of CAE Research Artificial Intelligence for Engineering
Design, Analysis, and Manufacturing, vol. 8, no. 4, pp. 275-282.
Abstract: Less than a decade ago it seemed that a new paradigm of
engineering-called computer-aided engineering (CAE)-was emerging, this
emergence being driven in part by the success of computer support for the
tasks of engineering analysis, and in part by a new understanding of how
computational ideas largely rooted in artificial intelligence (AI) could
perhaps improve the practice of engineering, especially in the area of
design synthesis. However, while this "revolution" has failed to
take root or flourish as a separate discipline, it has spawned research
that is very different from traditional engineering research. To the
extent that such CAE research is different in style and paradigm, it must
also be evaluated according to different metrics. This paper suggests some
of the metrics that can be used and points out some of the evaluation
issues that remain as open questions.
- Steinberg, L. (1994), Research
Methodology for AI and Design, Artificial Intelligence for Engineering
Design, Analysis, and Manufacturing, vol. 8, no. 4, pp. 283-287.
Abstract: This paper discusses my perspectives on research
methodology in the field of “AI and Design”. This perspective is based on
a view of a “Science of Design” focusing on methods of design and on
characteristics of design tasks that affect what methods are relevant for
a given task. The paper discusses two methodological issues: the need to
try applying a design method on multiple tasks and domains, and the need
to work with collaborators who are experts in the task domain of each
research system you build.
- Adelman,
L. and Gualtieri, J. and Riedel, S. L. (1994), A
multi-faceted approach to evaluating expert systems, Artificial
Intelligence for Engineering Design, Analysis, and Manufacturing, vol. 8,
no. 4, pp. 289-306.
Abstract: This paper overviews a multi-faceted approach to
evaluating expert systems. This approach has three facets: a technical
facet for "looking inside the black box", an empirical facet for
assessing the system's impact on performance, and a subjective facet for
obtaining users' judgment about the system. Such an approach is required
to test the system against the different types of criteria of interest to
sponsors and users, and is consistent with evolving life cycle paradigms.
Moreover, such an approach leads to the application of different
evaluation methods to answer the different types of evaluation questions. This paper overviews different evaluation methods for
each facet.
- Lowe, H. (1994), Proof
Planning: a Methodology for Developing AI Systems Incorporating Design
Issues, Artificial Intelligence for Engineering Design, Analysis, and
Manufacturing, vol. 8, no. 4, pp. 307-317.
Abstract: In cases where we can successfully express a domain
theory in a logical formalism and formulate a task in that domain in
mathematical terms, then this greatly facilitates the task of building
sound knowledge based systems. However, it is not immediately obvious how
the design aspects of such tasks, where these are an important
feature of problem-solving, can be incorporated in this approach. Design
issues differ from search problems in that there may be several choices,
each valid in some sense, but not (necessarily) equally good, or
equally appropriate in the current context. We describe a case study in
which we used a methodology based on the development of proof plans.
The ability to conduct our research according to the Popperian
framework of hypothesis, validation, testing, and modification in response
to empirical evidence --- the hypothetico-deductive
approach --- seems essential to any rigorous scientific endeavour. Proof planning, we believe, is a method
which readily exploits this inherently incrementalist
approach, and could prove a powerful tool in designing AI systems.
- Tomiyama,
T. (1994), From General Design Theory to knowledge intensive
engineering, Artificial Intelligence for Engineering Design, Analysis,
and Manufacturing, vol. 8, no. 4, pp. 319-333.
Abstract: This paper illustrates contributions of General Design
Theory (GDT) proposed by Yoshikawa to the development of advanced CAD
(Computer Aided Design) and to innovative design from research results of
our group at the University
of Tokyo. First, we
review GDT that formalizes design knowledge based on axiomatic set theory.
Second, this theoretical result is tested against experimental work on
design processes. Although in principle theoretical results agree with
experimental findings, some problems can be pointed out. From these
problems, we establish a new design process model, called refinement model, that has better agreement with experimental
findings. This model implies three guiding principles to develop a future
CAD system. One is that future CAD requires a mechanism for physics
modeling and multiple model management. Second, a mechanism for function modeling
is also required and the FBS (Function-Behavior-State) modeling is
proposed. Third, intention modeling is also proposed for recording
decision-making processes in design. These advanced modeling techniques
enable creative, innovative design. As an example, the design of
self-maintenance machines is illustrated. This design example utilizes
design knowledge intensively on a knowledge intensive CAD. This is a new
way of engineering and can be called knowledge intensive engineering. The
design of self-maintenance machines is, therefore, an example of knowledge
intensive design of knowledge intensive products, which demonstrates the
power of the design methodology derived from GDT.
- Garcia, A. C. B., Howard,
C. H., and Stefik, M. J. (1994), Improving
Design and Documentation by Using Partially Automated Synthesis,
Artificial Intelligence for Engineering Design, Analysis, and
Manufacturing, vol. 8, no. 4, pp. 335-354.
Abstract:One of the products of engineering,
besides constructed artifacts, is design documentation. To understand how
design participants use documentation, we interviewed designers and
typical documentation users and also took protocols of them both creating
and using design documentation. Our protocols were taken from realistic
projects of preliminary design for heating, ventilation and air
conditioning systems (HVAC). Our studies of document creation and use
revealed three important issues: (1) Design participants not only look up
design facts; they frequently access documents to obtain information about
the rationale for design decisions; (2) The design rationale that they
seek is often missing from the documents; (3) design requirements change
frequently over a project life cycle so that design documents are often
inconsistent and out-of-date. Recognizing these documentation issues in
design practice, we developed a new approach in which documents are no
longer static records, but rather interactive design models supporting a
case. We demonstrated the feasibility of the approach by constructing a
running system and testing it designers on realistic problems. We also
analyze the costs and benefits of creating and using documentation of
design rationale and of the active documents approach in particular for
routine, preliminary design in domains where community practice is widely
shared and largely standardized. The approach depends on the feasibility
of creating a parametric design model for the design domain.
- Reich, Y. (1994), “Annotated bibliography on
research methodology,” Artificial Intelligence for Engineering
Design, Analysis, and Manufacturing, vol. 8, no. 4, pp. 355-366.
Copyright ©
1997-2005 Yoram Reich
Page URL: http://www.eng.tau.ac.il/~yoram/topics/aiedam-method.html
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modified: 5/6/2005 6:18:00 PM