Hostname: page-component-6766d58669-kn6lq Total loading time: 0 Render date: 2026-05-23T00:19:07.836Z Has data issue: false hasContentIssue false
  • English
  • Français

Mixed quantitative/qualitative method for evaluating compromise solutions to conflicts in collaborative design

Published online by Cambridge University Press:  27 February 2009

Dennis Bahler ,
Catherine Dupont  and
James Bowen
Dennis Bahler
Affiliation:
Department of Computer Science, North Carolina State University, Raleigh, NC 27695-8206, U.S.A.
Catherine Dupont
Affiliation:
Bell Northern Research, Research Triangle Park, NC 27709, U.S.A.
James Bowen
Affiliation:
Department of Computer Science, National University of Ireland, Cork, Ireland
Get access Rights & Permissions [Opens in a new window]

Abstract

Conflicts are likely to arise among participants in a collaborative design process as the inevitable outgrowth of the differing perspectives and viewpoints involved. The opportunities for conflict are magnified if many perspectives are brought to bear on a common artifact early in the design process, as in concurrent engineering or integrated engineering. Design advice tools can assist in the process of resolving these conflicts by making critiques and suggestions conveniently available to design participants, and by offering a fair means of evaluating and comparing suggested alternatives for compromise solution. In previous work we introduced a protocol based on notions of economic utility by which design advice systems can recognize conflict and mediate negotiation fairly. This protocol allowed design teams to express the desire to maximize or minimize the values of design parameters over totally ordered bounded domains of values, such as real numeric intervals. In this paper we extend this approach by allowing expressed preferences of design teams to be qualitative as well as quantitative, by allowing teams to express interest in parameters before they actually come into existence, and by relaxing many other of the earlier restrictions on the ways teams may express their preferences.

Keywords

NegotiationConcurrent EngineeringUtilityDesign ConstraintsDesign Advice Systems

Information

Type
Articles
Information
AI EDAM , Volume 9 , Issue 4 , September 1995 , pp. 325 - 336
Copyright
Copyright © Cambridge University Press 1995

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.)

Article purchase

Temporarily unavailable

References

REFERENCES

Bahler, D., & Bowen, J. (1995). Constraint logic and its application in production: An implementation using the Galileo4 language and system. In Production and Scheduling of Manufacturing Systems, (Artiba, A. and Elmaghraby, S.E., Eds.), pp. 7–95. Chapman and Hall, London.Google Scholar
Bahler, D., Dupont, C., & Bowen, J. (1993). Negotiation in clientassisted inference systems for Concurrent Engineering. In Workshop on Computational Models of Conflict Management in Cooperative Problem Solving, Chambery, France, 23–40.Google Scholar
Bahler, D., Dupont, C., & Bowen, J. (1994a). An axiomatic approach that supports negotiated resolution of design conflicts in concurrent engineering. In Artificial Intelligence in Design, (Gero, J.S., & Sudweeks, F., Eds.), pp. 363–379. Kluwer Academic Publishers, Dordrecht, The Netherlands.Google Scholar
Bahler, D., Dupont, C., & Bowen, J. (1994b). Mediating Conflict in Concurrent Engineering with a Protocol Based on Utility. Concurrent Engineering: Research and Applications 2, Special Issue on Conflict Management in Concurrent Engineering, 197–207.CrossRefGoogle Scholar
Bowen, J., & Bahler, D. (1991a). Conditional existence of variables in generalized constraint networks. In Proc. of 9th Nat. Conf. on Al (AAAI-91), Anaheim, CA, 215–220.Google Scholar
Bowen, J., & Bahler, D. (1991b). Supporting cooperation between multiple perspectives in a constraint-based approach to concurrent engineering. J. Design Manufact. 1, 89–105.Google Scholar
Bowen, J., & Bahler, D. (1992). Frames, quantification, perspectives and negotiation in constraint networks for life-cycle engineering. Int. J. Al Eng. 7, 199–226.Google Scholar
Bowen, J., & Bahler, D. (1993). Constraint-based software for concurrent engineering. IEEE Computer 26, Special Issue on Computer Support for Concurrent Engineering, 66–68.Google Scholar
Cutkosky, M., Engelmore, R., Fikes, R., Gruber, T., Genesereth, M., Mark, W., Tenenbaum, J., & Weber, J. (1993). PACT: An experiment in integrating concurrent engineering systems. IEEE Computer 26, Special Issue on Computer Support for Concurrent Engineering, 28–37.Google Scholar
Daboni, L., Montesano, A., & Lines, M. (Eds.) (1986). Recent developments in the foundations of utility and risk theory. D. Reidel, Dordrecht, The Netherlands.CrossRefGoogle Scholar
Erman, L.D., Lark, J.S., & Hayes-Roth, F. (1988). ABE: An environment for engineering intelligent systems. IEEE Transactions on Software Engineering SE-14, 1758–1769.Google Scholar
Fishburn, P.C. (1988). Nonlinear preference and utility theory. Johns Hopkins University Press, Baltimore.Google Scholar
Garcia, A.C.B., & Howard, H.C. (1992). ADD's model for acquiring design rationale. In Proc. AAAI '92 Workshop on Design Rationale Capture and Use, San Jose, CA, 91–98.Google Scholar
Gasser, L., Braganza, C., & Herman, N. (1987). MACE: A flexible testbed for distributed AI research. In Distributed Artificial Intelligence, (Huhns, M.N., Ed.), 119–149, Morgan Kaufmann, Los Altos, CA.Google Scholar
Gruber, T.R., Tenenbaum, J.M., & Weber, J.C. (1992). Toward a knowledge medium for collaborative product development. In Artificial Intelligence in Design '92, (Gero, J.S., Ed.), pp. 413–432, Kluwer Academic Publishers, Dordrecht, The Netherlands.Google Scholar
Hillier, F.S., & Lieberman, G.J. (1990). Introduction to operations research, McGraw Hill, New York.Google Scholar
Keeney, R.L., & Raiffa, H. (1976). Decisions with multiple objectives. Wiley, New York.Google Scholar
Petrie, C. (1993). The REDUX' server. In Proc. Int. Conf. Intelligent and Cooperative Information Systems (ICICIS), Rotterdam.CrossRefGoogle Scholar
Rosenschein, J.S., & Genesereth, M.R. (1985). Deals among rational agents. In Proc. 9th Int. Joint Conf. on AI (IJCAI-85), Los Angeles, 91–99.Google Scholar
Samuelson, P.A., & Nordhaus, W.D. (1992). Economics. 14th ed., McGraw Hill, New York.Google Scholar
Sycara, K.P. (1989). Multiagent compromise via negotiation. In Distributed Artificial Intelligence 2, (Gasser, L., and Huhns, M.N., Eds.), pp. 119–138, Pitman/Morgan Kaufmann, London.Google Scholar
Tenenbaum, J., Weber, J., & Gruber, T. (1992). Enterprise integration: Lessons from SHADE and PACT. In Enterprise Integration Modeling, (Petrie, C., Ed.), MIT Press, Cambridge, MA.Google Scholar
Tversky, A., & Kahneman, D. (1981). The framing of decisions and the psychology of choice. Science 211, 453–58.Google Scholar
Werkman, K.J. (1991). Using negotiation as a means of coordinating distributed problem solving. In Proc. World Congress on Expert Systems, Orlando, FL.Google Scholar
Werkman, K.J. (1992). Cooperative design evaluation between multiple agents using negotiation with shareable perspectives. IBM Corporation Monograph.Google Scholar
Wellman, M. (1994). A computational market model for distributed configurational design. In Proc. 12th Nat. Conf. on Artificial Intelligence (AAAI '94), 401–407.Google Scholar