Hostname: page-component-78c5997874-dh8gc Total loading time: 0 Render date: 2024-11-05T11:29:03.990Z Has data issue: false hasContentIssue false
  • English
  • Français

Layered models of research methodologies

Published online by Cambridge University Press:  27 February 2009

Yoram Reich
Yoram Reich
Affiliation:
Department of Solid Mechanics, Materials, and Structures, Faculty of Engineering, Tel Aviv University, Tel Aviv 69978, Israel.
Get access Rights & Permissions [Opens in a new window]

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. Concepts from the philosophy of science are introduced, and models of world views of science are built on them. These world views are combined with research heuristics or research perspectives and criteria for evaluating research to create a layered model of research methodology. This layered model can serve to organize and facilitate a better understanding of future studies of research methodologies. Many of the issues involved in the study of AI and AIEDAM research methodology using this layered model are discussed.

Keywords

SciencePracticeResearch ProcessResearch HeuristicsEvaluation Criteria
Type
Articles
Information
AI EDAM , Volume 8 , Issue 4 , Fall 1994 , pp. 263 - 274
Copyright
Copyright © Cambridge University Press 1994

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Addis, W. (1990). Structural Engineering: The Nature of Theory and Design. Ellis Horwood, New York.Google Scholar
Adelman, L. (1991). Experiments, quasi-experiments, and case studies: A review of empirical methods for evaluating decision support systems. IEEE Trans. Systems, Man, and Cybernetics 21(2), 293–301.CrossRefGoogle Scholar
Adelman, L., Gualtieri, J., & Riedel, S. L. (1994). A multi-faceted approach to evaluating expert systems. Artificial Intelligence in Engineering Design, Analysis, and Manufacturing 8(4).Google Scholar
Agassi, J. (1975). Science in Flux. D. Reidel Publishing Company, Dordrecht.CrossRefGoogle Scholar
Argyris, C. (1980). Inner Contradictions of Rigorous Research. Academic Press, New York.Google Scholar
Bailey, M.T. (1992). Do physicists use case studies? Thoughts on public administration. Public Administration Rev. 52(1), 47–54.CrossRefGoogle Scholar
Bareiss, R. (1989). Exemplar-Based Knowledge Acquisition. Academic Press, Boston, Massachusetts.Google Scholar
Bartley, W.W.I. (1962). The Retreat to Commitment. A.A. Knopf, New York.Google Scholar
Bernstein, R.J. (1992). The New Constellation: The Ethical-Political Horizons of Modernity/Postmodernity. MIT Press, Cambridge, Massachusetts.Google Scholar
Bjerknes, G., Ehn, P., & Kyng, M., Eds. (1987). Computers and Democracy: A Scandinavian Challenge. Gower Press, Brookfield, Vermont.Google Scholar
Blumberg, M., & Pringle, C.D. (1983). How control groups can cause loss of control in action research: The case of Rushton Coal Mine. J. Appl. Behavioral Sci. 19(4), 409–425.CrossRefGoogle Scholar
Brinberg, D., Lynch, J., & Sawyer, A.G. (1992). Hypothesized and confounded explanations in theory. J. Consumer Res. 19(2), 139–154.CrossRefGoogle Scholar
Brooks, R.A. (1991). New approaches to robotics. Science 253(5025), 1227–1232.CrossRefGoogle ScholarPubMed
Bundy, A. (1988). The use of explicit plans to guide inductive proofs. In Proc. 9th Conf. on Automated Deduction, 111–120.CrossRefGoogle Scholar
Bundy, A. (1990). What kind of filed is AI? In The Foundations of Artificial Intelligence: A Sourcebook, (Partridge, D., & Wilks, Y., Eds.), pp. 215–222. Cambridge University Press, Cambridge, UK.CrossRefGoogle Scholar
Bundy, A., & Ohlsson, S. (1990). The nature of AI principles: A debate in the AISB Quarterly. In The Foundations of Artificial Intelligence: A Sourcebook, (Partridge, D., & Wilks, Y., Eds.), pp. 135–154. Cambridge University Press, Cambridge, UK.CrossRefGoogle Scholar
Bunge, M. (1983). Treatise on Basic Philosophy, Volume 5, Epistemology and Methodology I: Understanding the World. D. Reidel Publishing Company, Dordrecht.CrossRefGoogle Scholar
Buntine, W. (1990). Myths and legends in learning classification rules. In Proc. AAAI-90, Boston, Massachusetts, pp. 736–742. AAAI Press, Menlo Park, CA.Google Scholar
Campbell, J.A. (1990). Three novelties of AI: Theories, programs and rational reconstruction. In The Foundations of Artificial Intelligence: A Sourcebook, (Partridge, D., & Wilks, Y., Eds.), pp. 237–246. Cambridge University Press, Cambridge, UK.CrossRefGoogle Scholar
Carroll, J.M., Ed. (1991). Designing Interaction: Psychology at the Human-Computer Interface. Cambridge University Press, Cambridge, UK.Google Scholar
Chalmers, D.J., French, R.M., & Hofstadter, D.R. (1992). High-level perception, representation, and analogy: A critique of artificial intelligence methodology. J. Experimental and Theoretical Artificial Intelligence 4(3), 185–211.CrossRefGoogle Scholar
Cohen, P.R., & Howe, A.E. (1988). How evaluation guides AI research. AI Magazine 9(4), 35–43.Google Scholar
Cohen, P.R., & Howe, A.E. (1989). Toward AI research methodology: Three case studies in evaluation. IEEE Trans. Systems, Man, and Cybernetics SMC- 19(3), 634–646.CrossRefGoogle Scholar
Cox, E. (1992). The great myths of fuzzy logic. AI Expert 7(1), 40–45.Google Scholar
Dietrich, E. (1990). Programs in the search for intelligent machines: The mistaken foundation of AI. In The Foundations of Artificial Intelligence: A Sourcebook, (Partridge, D., & Wilks, Y., Eds.), pp. 223–233. Cambridge University Press, Cambridge, UK.CrossRefGoogle Scholar
Dixon, J.R. (1987). On research methodology towards a scientific theory of engineering design. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 1(3), 145–157.CrossRefGoogle Scholar
Duhem, P. (1982). The Aim and Structure of Physical Theory. Princeton University Press, Princeton, New Jersey.Google Scholar
Dvorak, D.L. (1988). Guide to CL-Protos: An exemplar-based learning apprentice. Technical Report AI88–87, Artificial Intelligence Laboratory, The University of Texas at Austin, Austin Texas.Google Scholar
Dym, C.L., & Levitt, R.E. (1994). On the evolution of CAE research. Artificial Intelligence for Engineering Design, Analysis, and Manufacturing 8(4).CrossRefGoogle Scholar
Efron, B., & Tibshirani, R. (1991). Statistical data analysis in the computer age. Science 253, 390–395.CrossRefGoogle ScholarPubMed
Farias, (1989). Heidegger and Nazism. Temple University Press, Philadelphia, Pennsylvania.Google Scholar
Fenves, S.J., Garrett, J.H.J., & Hakim, M.M. (1994). Representation and processing of design standards: A bifurcation between research and practice. In Proc. 1994 Structures Congress, Atlanta, Georgia, ASCE, New York.Google Scholar
Feyerabend, P.K. (1975). Against Method. New Left Books, London, UK.Google Scholar
Floyd, C., Züllinghoven, H., Budde, R., & Keil-Slawik, R., Eds. (1992). Software Development and Reality Construction. Springer-Verlag, Berlin.CrossRefGoogle Scholar
Fox, M.S. (1990). AI and expert systems myths, legends, and facts. IEEE Expert 5(1), 8–20.CrossRefGoogle Scholar
Genesereth, M.R., & Nilsson, N.J. (1987). Logical Foundations of Artificial Intelligence. Morgan Kaufmann, Los Altos, California.Google Scholar
Giere, R.N. (1984). Understanding Scientific Reasoning, 2nd ed. Holt, Rinehart and Winston, New York.Google Scholar
Gille, B. (1966). Engineers of the Renaissance. MIT Press, Cambridge, Massachusetts.Google Scholar
Grabiner, J.V. (1986). Computers and the nature of man: A historian’s perspective on controversies about artificial intelligence. Bull. Amer. Math. Soc. 15(2), 113–126.CrossRefGoogle Scholar
Guba, E.G. (1990 a). The alternative paradigm dialog. In The Paradigm Dialog, (Guba, E.G., Ed.), pp. 17–27. Sage Publications, Newbury Park, California.Google Scholar
Guba, E.G., Ed. (1990 b). The Paradigm Dialog. Sage Publications, Newbury Park, California.Google Scholar
Habermas, J. (1971). Knowledge and Human Interests (translated by Shapiro, J.J.). Beacon Press, Boston, Massachusetts.Google Scholar
Hall, R.P., & Kibler, D.F. (1985). Differing methodological perspectives in artificial intelligence. AI Magazine 6(3), 166–178.Google Scholar
Halpern, M. (1987). Turing’s test and the ideology of artificial intelligence. Artificial Intelligence Rev. 1(2), 79–93.CrossRefGoogle Scholar
Hayes, P.J., Novack, G.S. Jr, & Lehnert, W.G. (1992). ACM forum: In defence of artificial intelligence. Communications of the ACM 35(12), 13–14.Google Scholar
Hayes-Roth, F., & Fikes, R. (1991). Interviews by B. Chandrasekaran. IEEE Expert 6(5), 3–14.Google Scholar
Hooke, R. (1983). How to Tell the Liars from the Statisticians. Marcel Dekker, New York.Google Scholar
Kerr, A.D., & Pipes, R.B. (1987). Why we need hands-on engineering education. Technol. Rev. Oct., 36–42.Google Scholar
Kuhn, T.S. (1962). The Structure of Scientific Revolution. The University of Chicago Press, Chicago, Illinois.Google Scholar
Kuhn, T.S. (1987). Objectivity, value judgment, and theory choice.In Scientific Knowledge: Basic Issues in the Philosophy of Science, (Kourany, J.A., Ed.), pp. 197–207. Wadsworth, Belmont, California.Google Scholar
Laird, J.E., Newell, A., & Rosenbloom, P.S. (1987). Soar: an architecture for general intelligence. Artificial Intelligence 33(1), 1–64.CrossRefGoogle Scholar
Lakatos, I. (1968). Criticism and the methodology of scientific research programmes. In Proc. Aristotelian Society 69, 149–186.CrossRefGoogle Scholar
Lenat, D., Prakash, M., & Shepherd, M. (1986). CYC: Using common sense knowledge to overcome brittleness and knowledge acquisition bottlenecks. AI Magazine 6(4), 65–85.Google Scholar
Lenat, D.B., & Brown, J.S. (1984). Why am and eurisco appear to work. Artificial Intelligence 23(3), 269–294.CrossRefGoogle Scholar
Levi, K. (1989). Expert systems should be more accurate than human experts: Evaluation procedures from human judgment and decisionmaking. IEEE Trans. Systems, Man, and Cybernetics SMC-19(3), 647–657.CrossRefGoogle Scholar
Liebowitz, J. (1987). Common fallacies about expert systems. Computers and Society 16(4), 28–33.CrossRefGoogle Scholar
Lowe, H. (1994). Proof planning: A methodology for developing AI systems incorporating design issues. Artificial Intelligence for Engineering Design, Analysis, and Manufacturing 8(4).CrossRefGoogle Scholar
Marques, D., Dallemagne, G., Klinker, G., McDermott, J., & Tung, D. (1992). Easy programming: Empowering people to build their own applications. IEEE Expert 7(3), 16–29.CrossRefGoogle Scholar
Marr, D. (1982). Vision: A Computational Investigation into the Human Representation and Processing of Visual Information. W.H. Freeman, San Francisco, California.Google Scholar
McDermott, D. (1981). Artificial intelligence meets natural stupidity. In Mind Design, (Haugeland, J., Ed.), pp. 143–160. MIT Press, Cambridge, Massachusetts.Google Scholar
McDermott, J. (1994). Situating software artifacts. Presented at the AI Seminar, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, April, 1994.Google Scholar
Mettrey, W. (1992). Expert systems and tools: Myths and realities. IEEE Expert 7(1), 4–12.CrossRefGoogle Scholar
Moses, J. (1971). Symbolic integration: The stormy decade. Communication of the ACM 14(8), 548–560.CrossRefGoogle Scholar
Muller, M.J., Kuhn, S., & Meskill, J.A., Eds. (1992). PDC’92: Proc. Participatory Design Conf., Cambridge, Massachusetts. Computer Professionals for Social Responsibility, Palo Alto, California.Google Scholar
National Research Council (1991). Improving Engineering Design: Designing For Competitive Advantage. National Academy Press, Washington, DC.Google Scholar
Nazareth, D.L., & Kennedy, M.H. (1993). Knowledge-based system verification, validation, and testing: The evolution of a discipline. Int. J. Expert Systems 6(2), 143–162.Google Scholar
Newell, A. (1983). Intellectual issues in the history of artificial intelligence. In The Study of Information: Interdisciplinary Messages, (Machlup, F., & Mansfield, U., Eds.), pp. 187–227. John Wiley & Sons, New York.Google Scholar
Newell, A., & Simon, H.A. (1976). Computer science as empirical inquiry: Symbols and search. Communication of the ACM 19, 113–126.CrossRefGoogle Scholar
Palumbo, D.J., & Calista, D.J., Eds. (1990). Implementation and The Policy Process: Opening Up The Black Box. Greenwood Press, New York.Google Scholar
Pardee, W.J., Shaff, M.A., & Hayes-Roth, B. (1990). Intelligent control of complex materials processes. AI EDAM 4(1), 55–56.Google Scholar
Partridge, D., & Wilks, Y., Eds. (1990). The Foundations of Artificial Intelligence: A Sourcebook. Cambridge University Press, Cambridge, UK.CrossRefGoogle Scholar
Pearce, M., Goel, A.K., Kolodner, J.L., Zimring, C., Sentosa, L., & Billington, R. (1992). Case-based design support: A case study in architectural design. IEEE Expert 7(5), 14–20.CrossRefGoogle Scholar
Petroski, H. (1992). The Evolution of Useful Things. Knopf, New York.Google Scholar
Pitt, J.C. (1992). Galileo, Human Knowledge, and the Book of Nature: Method Replaces Metaphysics. Kluwer Academic Publishers, Dordrecht.CrossRefGoogle Scholar
Pople, H. (1985). Evolution of an expert system: From Internist to Caduceus. In AI in Medicine, (De Lotto, I., & Stefanelli, M., Eds.), pp. 179–208. Elsevier, Amsterdam.Google Scholar
Pylyshyn, Z.W. (1991). Some remarks on the theory-practice gap. In De-signing Interaction: Psychology at the Human-Computer Interface, (Carroll, J.M., Ed.), pp. 39–49. Cambridge University Press, Cambridge, UK.Google Scholar
Reason, P., Ed. (1988). Human Inquiry in Action: Developments in New Paradigm Research. Sage Publications, Newbury Park, California.Google Scholar
Reason, P., & Rowan, J., Eds. (1981). Human Inquiry: A Sourcebook of New Paradigm Research. John Wiley & Sons, New York.Google Scholar
Reich, Y. (1991). Book review of Exemplar-Based Knowledge Acquisition, by Ray Bareiss (Academic Press, 1989). Machine Learning 6(1), 99–103.CrossRefGoogle Scholar
Reich, Y. (1992). Transcending the theory-practice problem of technology. Technical Report EDRC 12–51–92, Engineering Design Research Center, Carnegie Mellon University, Pittsburgh, Pennsylvania.Google Scholar
Reich, Y. (1993 a). The development of BRIDOER: A methodological study of research on the use of machine learning in design. Artificial Intelligence in Engineering 8(3), 217–231.CrossRefGoogle Scholar
Reich, Y. (1993 b). The study of design research methodology. J. Mechanical Design, ASME. (accepted for publication).Google Scholar
Reich, Y. (1994). What is wrong with CAE and can it be fixed? In Preprints of Bridging the Generations: An International Workshop on the Future Directions of Computer-Aided Engineering. Department of Civil Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania.Google Scholar
Ritchie, G.D., & Hanna, F.K. (1984). AM: A case study in AI methodology. Artificial Intelligence 23(3), 249–268.CrossRefGoogle Scholar
Rowan, J. (1981). A dialectical paradigm for research. In Human Inquiry: A Sourcebook of New Paradigm Research, Reason, P., & Rowan, J., Eds.), pp. 93–112. John Wiley & Sons, New York.Google Scholar
Schön, D.A. (1983). The Reflective Practitioner: How Professionals Think in Action. Temple Smith, London, UK.Google Scholar
Schumm, S.A. (1991). To Interpret the Earth: Ten Ways to be Wrong. Cambridge University Press, Cambridge, UK.Google Scholar
Sharkey, N.E., & Brown, G.D.A. (1986). Why artificial intelligence needs an empirical foundation. In AI: Principles and Applications, (Yazdani, M., Ed.), pp. 260–291. Chapman and Hall, London, UK.Google Scholar
Shvyrkov, V.V. (1987). What Harvard statisticians don’t tell us. Quality & Quantity 21(4), 335–347.CrossRefGoogle Scholar
Sloane, S.B. (1991). The use of artificial intelligence by the United States Navy: Case study of a failure. AI Magazine 12(1), 80–92.Google Scholar
Smith, C.N., & Dainty, P., Eds. (1991). The Management Research Handbook. Routledge, London, UK.Google Scholar
Steinberg, L. (1994). Research methodology for AI and design. Artificial Intelligence for Engineering Design, Analysis, and Manufacturing 8(4).CrossRefGoogle Scholar
Tomiyama, T. (1994). From General Design Theory to knowledge intensive engineering. Artificial Intelligence for Engineering Design, Analysis, and Manufacturing 8(4).CrossRefGoogle Scholar
Truesdell, C. (1982). The disastrous effects of experiment upon the early development of thermodynamics. In Scientific Philosophy Today: Essays in Honor of Mario Bunge, (Agassi, J., & Cohen, R.S., Eds.), pp. 415–423. D. Reidel Publishing Company, Dordrecht.Google Scholar
Ullman, D. (1991). Current status of design research in the US. In Proc. ICED-91, Zurich. Heurista, Zurich.Google Scholar
Vincenti, W.G. (1990). What Engineers Know and How They Know It: Analytical Studies From Aeronautical History. Johns Hopkins University Press, Baltimore, Maryland.Google Scholar
Waring, S.P. (1991). Taylorism Transformed: Scientific Management Theory Since 1945. The University of North Carolina Press, Chapel Hill, North Carolina.Google Scholar
Weimer, W.B. (1979). Notes on the Methodology of Scientific Research. Lawrence Erlbaum, Hillsdale, New Jersey.Google Scholar
Weizenbaum, J. (1976). Computer Power and Human Reason: From Judgement to Calculation. Freeman, San Francisco, California.Google Scholar
West, D.M., & Travis, L.E. (1991). The computational metaphor and artificial intelligence: A reflective examination of a theoretical falsework. AI Magazine 12(1), 64–79.Google Scholar
Whyte, W.F., Ed. (1991). Participatory Action Research. Sage Publications, Newbury Park, California.CrossRefGoogle Scholar
Wilkes, M.V. (1992). Artificial intelligence as the year 2000 approaches. Communications of the ACM 35(8), 17–20.CrossRefGoogle Scholar
Winograd, T. (1990). Thinking machines: Can there be? Are we? In The Foundations of Artificial Intelligence: A Sourcebook, (Partridge, D., & Wilks, Y., Eds.), pp. 167–189. Cambridge University Press, Cambridge, UK.CrossRefGoogle Scholar
Winograd, T., & Flores, F. (1986). Understanding Computers and Cognition: A New Foundation For Design. Albex Publishing, Norwood, New Jersey.Google Scholar
Wolin, R. (1990). The Politics of Being: The Political Thought of Martin Heidegger. Columbia University Press, New York.CrossRefGoogle Scholar