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Annotated bibliography on 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
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Abstract

This annotated bibliography includes a small sample of sources on various aspects of research methodology from diverse disciplines that influence research on artificial intelligence techniques in engineering design analysis and manufacturing (AIEDAM). Some of these sources are extended edited volumes containing many relevant contributions and pointing to additional references. These volumes are marked by a preceding bullet (•). The bibliography is not comprehensive; it covers only several important subjects, and in each subject it lists several representative contributions ordered chronologically.

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AI EDAM , Volume 8 , Issue 4 , Fall 1994 , pp. 355 - 366
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Copyright © Cambridge University Press 1994

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References

LIST OF 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.Google 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
Agarwal, R., & Tanniru, M. (1990). Systems development life-cycle for expert systems. Knowledge-Based Systems 3 (3), 170–180.Google Scholar
Aldag, R.J., & Stearns, T.M. (1988). Issues in research methodology. J. Management 14 (2), 253–276.Google Scholar
Amarel, S. (1989). Artificial intelligence and design: Opportunities, research problems and directions. Technical Report LCSR-TR-124, Rutgers University, New Brunswick, New Jersey.Google Scholar
Amsterdam, J. (1988). Some philosophical problems with formal learning theory. In Proc. of AAAI-88, St. Paul, Minnesota, pp. 580–584. Morgan Kaufmann, San Mateo, California.Google Scholar
Angluin, D., & Smith, C.H. (1983). Inductive inference: Theory and methods. Computing Surveys 15 (3), 237–269.CrossRefGoogle Scholar
Antonsson, E.K. (1987). Development and testing of hypotheses in engineering design research. ASME J. Mechanisms, Transmissions, and Automation in Design 109, 153–154.CrossRefGoogle Scholar
Arbib, M.A., & Hesse, M.B. (1986). The Construction of Reality. Cambridge University Press, Cambridge, UK.Google Scholar
Argyris, C. (1980). Inner Contradictions of Rigorous Research. Academic Press, New York.Google Scholar
Ayel, M., & Laurent, J.-P., Eds. (1991). Validation, Verification and Test of Knowledge-Based Systems. John Wiley & Sons, New York.Google Scholar
Bachant, J., & McDermott, J. (1984). Rl revisited: Four years in the trenches. AI Magazine 5 (3), 21–32.Google Scholar
Bailey, M.T. (1992). Do physicists use case studies? Thoughts on public administration. Public Administration Review 52 (1), 47–54.CrossRefGoogle Scholar
Barki, H., & Hartwick, J. (1989). Rethinking the concept of user involvement. MIS Quarterly 13 (1), 53–63.Google Scholar
Baronas, A.-M.K., & Louis, M.R. (1988). Restoring a sense of control during implementation: How user involvement leads to system acceptance. MIS Quarterly 12 (1), 111–123.Google Scholar
Benbasat, I., Goldstein, D.K., & Mead, M. (1987). The case research strategy in studies of information systems. MIS Quarterly 11 (3), 369–386.Google Scholar
Blissett, M. (1972). Politics in Science. Little, Brown and Company, Boston, Massachusetts.Google Scholar
Bloombaum, M. (1991). Influence of research design upon data analysis. Quality & Quantity 25 (3), 327–331.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. Applied Behavioral Science 19 (4), 409–425.Google Scholar
Bowers, J.M., & Benford, S.D., Eds. (1991). Studies in Computer Supported Cooperative Work: Theory, Practice and Design. North-Holland, Amsterdam.Google Scholar
Brinberg, D., Lynch, J., & Sawyer, A.G. (1992). Hypothesized and confounded explanations in theory. J. Consumer Research 19 (2), 139–154.CrossRefGoogle Scholar
Bundy, A. (1987). How to improve the reliability of expert systems. Technical Report DAI Research Paper No. 336, Department of Artificial Intelligence, University of Edinburgh, Edinburgh, Scotland.Google Scholar
Bundy, A. (1988). Artificial intelligence: Art or science? Technical Report DAI Research Paper No. 358, Department of Artificial Intelligence, University of Edinburgh, Edinburgh, Scotland.Google 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.Google Scholar
Bunge, M. (1983). Treatise on Basic Philosophy, Volume 5, Epistemology and Methodology I: Understanding the World. D. Reidel Publishing Company, Dordrecht.Google Scholar
Buntine, W. (1989). A critique of the Valiant model. In Proc. Eleventh Int. Joint Conf. on Artificial Intelligence, pp. 837–842. Morgan Kaufmann, Detroit, Michigan.Google Scholar
Buntine, W. (1990). Myths and legends in learning classification rules. In Proc. AAAI-90, Boston, Massachusetts, pp. 736–742. AAAI Press, Menlo Park, California.Google Scholar
Carroll, J.M., Ed. (1991). Designing Interaction: Psychology at the Human-Computer Interface. Cambridge University Press, Cambridge, UK.Google Scholar
Casti, J.L. (1989). Paradigm Lost. Avon Books, New York.Google Scholar
Chu, P.C., & Elam, J.J. (1990). Induced system restrictiveness: An experimental demonstration. IEEE Trans. Systems, Man, and Cybernetics 20 (1), 195–201.CrossRefGoogle Scholar
Churchill, E., & Walsh, T. (1991). Scruffy but neat? (ai research methodology). AISB Quarterly 77, 8–9.Google Scholar
Clancey, W.J. (1986). From GUIDON to NEOMYCIN and HERACLES in twenty short lessons: ONR final report 1979–1985. AI Magazine 7 (3), 40–60.Google Scholar
Cohen, P.R. (1991). A survey of the eight national conference on artificial intelligence: Pulling together or pulling apart? AI Magazine 12 (1), 16–41.Google 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.Google Scholar
Corbett, J.M., Rasmussen, L.B., & Rauner, F. (1991). Crossing the Border: The Social and Engineering Design of Computer Integrated Manufacturing Systems. Springer-Verlag, Berlin.Google Scholar
DeMillo, R.A., Lipton, R.J., & Perlis, A.J. (1979). Social processes and proofs of theorems and programs. Communication of the ACM 22, 271–280.CrossRefGoogle 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.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).Google Scholar
Efron, B., & Tibshirani, R. (1991). Statistical data analysis in the computer age. Science 253, 390–395.CrossRefGoogle ScholarPubMed
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
Fishlock, D. (1975). The Business of Science. John Wiley & Sons, New York.Google Scholar
Floyd, C., Zullinghoven, H., Budde, R., & Keil-Slawik, R., Eds. (1992). Software Development and Reality Construction. Springer-Verlag, Berlin.Google Scholar
Gallupe, R.B., DeSanctis, G., & Dickson, G.W. (1988). Computer-based support for group problem-finding: An experimental investigation. MIS Quarterly 12 (2), 277–296.Google Scholar
Garcia, A.C.B., Howard, H.C., & Stefik, M.J. (1994). Improving design and documentation by using partially automated synthesis. Artificial Intelligence for Engineering Design, Analysis, and Manufacturing 8 (4).Google Scholar
Gevarter, W.B. (1987). The nature and evaluation of commercial expert system building tools. Computer May, 24–41.Google Scholar
Gilbert, G.N., & Heath, C., Eds. (1985). Social Action and Artificial Intelligence. Gower, Brookfield, VE.Google Scholar
Gilliers, D., Ed. (1992). Revolutions in Mathematics. Clarendon Press, Oxford, UK.Google Scholar
Göranzon, B., & Josefson, I., Eds. (1988). Knowledge, Skill and Artificial Intelligence. Springer-Verlag, Berlin.CrossRefGoogle Scholar
Gray, P., Vogel, D., & Beauclair, R. (1990). Assessing GDSS empirical research. European J. Operational Research 46 (2), 162–176.CrossRefGoogle Scholar
Greenberg, S., Ed. (1991). Computer-Supported Cooperative Work and Groupware. Harcourt Brace Jovanovich, London, UK.Google Scholar
Grinnell, F. (1982). The Scientific Attitude. Westview Press, Boulder, Colorado.Google Scholar
Guba, E.G., Ed. (1990). The Paradigm Dialog. Sage publications, New bury Park, California.Google Scholar
Guida, G., & Mauri, G. (1993). Evaluating performance and quality of knowledge-base systems: foundation and methodology. IEEE Trans. Knowledge and Data Engineering 5 (2), 204–224.Google Scholar
Gupta, U.G., Ed. (1991). Validating and Verifying Knowledge-Based Systems. IEEE Computer Society Press, Los Alamitos, California.Google Scholar
Habermas, J. (1971). Knowledge and Human Interests (translated by J.J. Shapiro). 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
Haugeland, J., Ed. (1981). Mind Design. MIT Press, Cambridge, Massachusetts.Google Scholar
Kaplan, B., & Duchon, D. (1988). Combining qualitative and quantitative methods in information systems research: a case study. MIS Quarterly 12 (4), 571–586.Google Scholar
Kibler, D., & Langley, P. (1988). Machine learning as an experimental science. In Proc. Third European Working Session on Learning, (Sleeman, D., Ed.), pp. 81–92. Pitman, Aberdeen.Google Scholar
Knorr-Cetina, K.D. (1981). The Manufacture of Knowledge: An Essay on the Constructivist and Contextual Nature of Science. Pergamon Press, Oxford, UK.Google Scholar
Konda, S., Monarch, I., Sargent, P., & Subrahmanian, E. (1992). Shared memory in design: A unifying theme for research and practice. Res. Engineering Design 4 (1), 23–42.Google Scholar
Kourany, J.A., Ed. (1987). Scientific Knowledge: Basic Issues in the Philosophy of Science. Wadsworth, Belmont, California.Google Scholar
Kuhn, T.S. (1962). The Structure of Scientific Revolution. The University of Chicago Press, Chicago, Illinois.Google Scholar
Lakatos, I. (1968). Criticism and the methodology of scientific research programmes. Proc. Aristotelian Society 69, 149–186.Google Scholar
Lee, A.S. (1989). A scientific methodology for MIS case studies. MIS Quarterly 13 (1), 33–50.Google Scholar
Lehner, P.E., & Adelman, L. (1989). Perspectives in knowledge engineering. IEEE Trans. Systems, Man, and Cybernetics 19 (3), 433–434.Google Scholar
Lenat, D.B., & Brown, J.S. (1984). Why am and eurisco appear to work. Artificial Intelligence 23 (3), 269–294.CrossRefGoogle Scholar
Lewin, K. (1946). Action research and minority problems. J. Social Issues 2 (4), 34–36.Google Scholar
Linster, M., Ed. (1992). Sisyphus ’92: Models of Problem Solving. Vol. 630 of Technical report of GMD. GMD, St. Augustin.Google 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).Google Scholar
Marcot, B. (1987). Testing your knowledge base. AI Expert 2, 42–47.Google 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.Google Scholar
Maso, I. (1989). The necessity of being flexible. Quality & Quantity 23 (2), 161–170.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, D.M., Mitchell, W., Schank, R.C., Chandrasekaran, B., & McDermott, J. (1985). The dark ages of AI: A panel discussion at AAAI-84. AI Magazine 6 (3), 122–134.Google Scholar
McDermott, J. (1982). Rl: A rule-based configurer of computer systems. Artificial Intelligence 19 (1), 39–88.Google Scholar
McDermott, J. (1986). Making expert systems explicit. In Information Processing 86, (Kugler, H.J., Ed.), pp. 539–544. North-Holland, Amsterdam.Google Scholar
McDermott, J. (1988). Preliminary steps toward a taxonomy of problem-solving methods. In Automating Knowledge Acquisition for Expert Systems, (Marcus, S., Ed.), pp. 225–266. Kluwer, Boston, Massachusetts.Google Scholar
McDermott, J. (1990). Developing software is like talking to eskimos about snow source. In Proc. AAAI-90, Boston, Massachusetts, pp. 1130–1133. AAAI Press, Menlo Park, California.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
McKevitt, P., & Partridge, D. (1991). Problem description and hypotheses testing in artificial intelligence. In AI and Cognitive Science ’90, (McTear, M.F., & Creaney, N., Eds.), pp. 26–47. Springer-Verlag, Menlo Park, California.Google Scholar
Mingers, J. (1989 a). An empirical comparison of pruning methods for decision-tree induction. Machine Learning 4 (2), 227–243.Google Scholar
Mingers, J. (1989 b). An empirical comparison of selection measures for decision-tree induction. Machine Learning 3 (4), 319–342.Google Scholar
Muster, D., & Mistree, F. (1990). Issues in engineering design research. Engineering Education, ASEE Dec, 1014–1016.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.Google Scholar
Nilsson, N.J. (1982). Artificial intelligence: Engineering, science, or slogan. AI Magazine 3 (1), 2–9.Google Scholar
Niwa, K., Sasaki, K., & Ihara, H. (1984). An experimental comparison of knowledge representation schemes. AI Magazine 5 (2), 29–36.Google Scholar
Nunamaker, J.F., Applegate, L.M., & Konsynski, B.R. (1988). Computer-aided deliberation: Model management and group decision support. Operations Res. 36 (6), 826–848.Google 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
Partridge, D. (1986). Engineering artificial intelligence software. Artificial Intelligence Rev. 1 (1), 27–41.Google Scholar
Partridge, D. (1987). Workshop on the foundations of AI: Final report. AI Magazine, 55–59.Google Scholar
Partridge, D., & Wilks, Y., Eds. (1990). The Foundations of Artificial Intelligence: A Sourcebook. Cambridge University Press, Cambridge, UK.Google Scholar
Pazzani, M.J., & Sarrett, W. (1992). A framework for average case analysis of conjunctive learning algorithms. Machine Learning 9 (4), 349–372.Google Scholar
Peters, R.H. (1991). A Critique of Ecology. Cambridge University Press, Cambridge, UK.Google Scholar
Pickering, A., Ed. (1992). Science as Practice and Culture. The University of Chicago Press, Chicago, Illinois.CrossRefGoogle Scholar
Pinsonneault, A., & Kraemer, K.L. (1990). The effects of electronic meetings on group processes and outcomes: An assessment of the empirical research. European J. Operational Res. 46 (2), 143–161.Google 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
Popper, K. (1965). Conjectures and Refutations: The Growth of Scientific Knowledge. Harper and Row, New York.Google Scholar
Preece, A.D., & Moseley, L. (1992). Empirical study of expert system development. Knowledge-Based Systems 5 (2), 137–148.Google Scholar
Preece, A.D., Shinghal, R., & Bataresh, A. (1992). Principles and practice in verifying rule-based systems. The Knowledge Engineering Review 7 (2), 115–141.Google Scholar
Prerau, D.S., Papp, W.L., Bhatnagar, R., & Weintraub, M. (1993). Verification and validation of expert systems: Experience with four diverse systems. Int. J. Expert Systems 6 (2), 251–269.Google Scholar
Pylyshyn, Z.W. (1991). Some remarks on the theory-practice gap. In Designing 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
Redner, H. (1987). The Ends of Science: An Essay in Scientific Authority. Westview Press, Boulder, Colorado.Google Scholar
Rehak, D.R., Ed. (1994). Bridging the Generations: International Workshop on the Future Directions of Computer-Aided Engineering. Department of Civil Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania.Google 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 BRIDCER: A methodological study of research on the use of machine learning in design. Artificial Intelligence in Engineering 8 (3), 217–231 (special issue on Machine Learning in Design).Google Scholar
Reich, Y. (1993 b). The study of design research methodology. J. Mechanical Design, ASME. (accepted for publication).Google Scholar
Reich, Y. (1993 c). The value of design knowledge. Knowledge Acquisition (accepted for publication).Google Scholar
Reich, Y. (1994 a). Layered models of research methodologies. Artificial Intelligence for Engineering Design, Analysis, and Manufacturing 8 (4).Google Scholar
Reich, Y. (1994 b). 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.Google Scholar
Rosenbrock, H.H., Ed. (1989). Designing Human-Centred Technology. Artificial Intelligence in Society. Springer-Verlag, Berlin.Google Scholar
Schank, R.C. (1987). What is AI, anyway? AI Magazine 8, 59–65.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
Schuster, J.A., & Yeo, R.R., Eds. (1986). The Politics and Rhetoric of Scientific Method. D. Reidel Publishing, Dordrecht.Google Scholar
Segre, A., Elkan, C., & Russell, A. (1991). A critical look at experimental evaluations of EBL. Machine Learning 6 (2), 183–195.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.Google Scholar
Shvyrkov, V., & Persidsky, A. (1991). The importance of being earnest in statistics. Quality & Quantity 25 (1), 19–28.Google 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
Smith, R.B. (1987). Linking quality & quantity: Part I. Understanding & explanation. Quality & Quantity 21 (3), 291–311.Google Scholar
Steinberg, L. (1994). Research methodology for AI and design. Artificial Intelligence for Engineering Design, Analysis, and Manufacturing 8 (4).Google Scholar
Tait, P., & Vessey, I. (1988). The effect of user involvement on system success: A contingency approach. MIS Quarterly 12 (1), 91–108.Google Scholar
Tatzlaff, L., & Mack, R.L. (1991). Discussion: perspectives on methodology in HC1 research and practice. In Designing Interaction: Psychology at the Human-Computer Interface, (Carroll, J.M., Ed.), pp. 286–314. Cambridge University Press, Cambridge, UK.Google Scholar
Thrun, S.B., Bala, J., Bloedorn, E., Bratko, I., Cestnik, B., Cheng, J., DeJong, K., Dzeroski, S., Fahlman, S.E., Fisher, D., Hamann, R., Kaufman, K., Keller, S., Kononenko, I., Kreuziger, J., Michalski, R.S., Mitchell, T., Pachowicz, P., Reich, Y., Vafaie, H., Van de Velde, W., Wenzel, W., Wnek, J., & Zhang, J. (1991). The MONK’s problems: A performance comparison of different learning algorithms. Technical Report CMU-CS-91–197, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania.Google Scholar
Tomiyama, T. (1994). General design theory. Artificial Intelligence for Engineering Design, Analysis, and Manufacturing 8 (4).Google Scholar
Toulmin, S. (1972). Human Understanding, Vol.1. Princeton University Press, Princeton, New Jersey.Google Scholar
Turney, P. (1991). The gap between abstract and concrete results in machine learning. J. Experimental and Theoretical Artificial Intelligence 3 (3), 179–190.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
Wegner, P. (1991). Paradigms of interpretation and modeling. Technical Report Cs-91–09, Department of Computer Science, Brown University, Providence, Rhode Island.Google Scholar
Weimer, W.B. (1979). Notes on the Methodology of Scientific Research. Lawrence Erlbaum, Hillsdale, New Jersey.Google Scholar
Weiss, S.M., & Kapouleas, I. (1989). An empirical comparison of pattern recognition, neural nets, and machine learning classification methods. In Proc. Eleventh Int. Joint Conf. on Artificial Intelligence, Detroit, Michigan, pp. 781–787. Morgan Kaufmann, San Mateo, California.Google Scholar
Weitzel, J.R., & Andrews, K.R. (1988). A company/university joint venture to build a knowledge-based system. MIS Quarterly 12 (1), 23–33.Google Scholar
West, D.M., & Travis, L.E. (1991). The computational metaphor and artificial intelligence: A reflective examination of a theoretical false-work. AI Magazine 12 (1), 64–79.Google Scholar
Whyte, W.F., Ed. (1991). Participatory Action Research. Sage Publications, Newbury Park, California.Google Scholar
Yazdani, M., & Narayanan, A., Eds. (1984). Artificial Intelligence: Human Effects. Ellis Horwood, Chichester, UK.Google Scholar
Ziman, J. (1984). An Introduction to Science Studies, The Philosophical and Social Aspects of Science and Technology. Cambridge University Press, Cambridge, UK.Google Scholar