Hostname: page-component-78c5997874-4rdpn Total loading time: 0 Render date: 2024-11-10T23:23:15.321Z Has data issue: false hasContentIssue false
  • English
  • Français

Interagent ties in team-based computational configuration design

Published online by Cambridge University Press:  08 April 2005

JESSE T. OLSON  and
JONATHAN CAGAN
JESSE T. OLSON
Affiliation:
Computational Design Lab, Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA
JONATHAN CAGAN
Affiliation:
Computational Design Lab, Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA
Get access Rights & Permissions [Opens in a new window]

Abstract

Organizational research has shown that effectively structuring the resources (human, informational, computational) available to an organization can significantly improve its collective computational capacity. Central to this improved capacity is the manner in which the organization's member agents are related. This study is an initial investigation into the role and potential of interagent ties in computational teaming. A computational team-based model, designed to more fully integrate agent ties, is created and presented. It is applied to a bulk manufacturing process-planning problem and its performance compared against a previously tested agent-based algorithm without these agent relationships. The performance of the new agent method showed significant improvement over the previous method: improving solution quality 280% and increasing solution identification per unit time an entire order of magnitude. A statistical examination of the new algorithm confirms that agent interdependencies are the strongest and most consistent performance effects leading to the observed improvements. This study illustrates that the interagent ties associated with team collaboration can be a highly effective method of improving computational design performance, and the results are promising indications that the application of organization constructs within a computational context may significantly improve computational problem solving.

Keywords

AgentsCollaborationComputational SynthesisConfiguration DesignTeams
Type
Research Article
Information
AI EDAM , Volume 18 , Issue 2 , May 2004 , pp. 135 - 152
Copyright
© 2004 Cambridge University Press

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

Antonsson, E.K. & Cagan, J., Eds. (2001). Formal Engineering Design Synthesis. New York: Cambridge University Press.CrossRef
Bar-Yam, Y. (1997). Dynamics of Complex Systems. Boston: Perseus Books.
Bar-Yam, Y. (2001). Introducing Complex Systems. Boston: NECSI Press.
Campbell, M.I., Cagan, J., & Kotovsky, K. (1999). A-design: An agent-based approach to conceptual design in a dynamic environment. Research in Engineering Design 11, 172–192.CrossRefGoogle Scholar
Campbell, M.I., Cagan, J., & Kotovsky, K. (2000). Agent-based synthesis of electromechanical design configurations. Journal of Mechanical Design 122, 61–69.CrossRefGoogle Scholar
Campbell, M.I., Cagan, J., & Kotovsky, K. (2003). The A-design approach to managing automated design synthesis. Research in Engineering Design 15.CrossRef
Carley, K. (2001a). Computational organizational science: A new frontier. Paper presented at the NAS Sackler Colloquium.
Carley, K. (2001b). Smart Agents and Organizations of the Future. Pittsburgh, PA: Carnegie Mellon University.
Carley, K. (2003). Intra-organizational computation and complexity. Working Paper, Carnegie Mellon University. Available on-line at www.heinz.cmu.edu/wpapers/retrievePDF?id=2000-15
Carley, K. & Hill, V. (2001). Structural change and learning within organizations. In Dynamics of Organizations: Computational Modeling and Organizational Theories (Lomi, A., Eds.), Cambridge, MA: MIT Press/AAAI Press/Live Oak.
Carley, K., Kjaer–Hansen, J., Newell, A., & Prietula, M. (1992). Plural-soar: A prolegomenon to artificial agents and organizational behavior. In Artificial Intelligence in Organization and Management Theory: Models of Distributed Activity (Michael, M. & Warglien, M., Eds.), pp. 87–118. New York: North–Holland.
Carley, K.M. & Gasser, L. (1999). Computational organization theory. In Distributed Artificial Intelligence (Weiss, G., Eds.), Chap. 7. Cambridge, MA: MIT Press.
Deshpande, S. & Cagan, J. (2003). An agent based optimization approach to manufacturing process planning. ASME Journal of Mechanical Design.
Fischer, C.E., Gunasekera, J., & Malas, J.C. (1997). Process model development for optimization of forged disk manufacturing processes. In Steel Forging: ASTM STP 1259 (Nisbett, E.G. & Melilli, A.S., Eds.), Vol. 2. Philadelphia, PA: American Society for Testing and Materials.
Gunasekera, J.S., Fischer, C.E., Malas, J.C., Mullins, W.M., & Yang, M.S. (1996). Development of process models for use with global optimization of a manufacturing system. Proc. ASME Symp. Modeling, Simulation and Control of Metal Processing. ASME International Mechanical Engineering Congress, Atlanta, GA, November.
Hustin, S. & Sangiovanni–Vicentelli, A. (1987). TIM, a new standard cell placement program based on the simulated annealing algorithm. IEEE Physical Design Workshop on Placement and Floorplanning, Hilton Head, SC, April.
Rich, E. (1983). Artificial Intelligence. New York: McGraw–Hill.
Wholey, D., Kiesler, S., & Carley, K. (1995). Learning Teamwork: Emergence of Communication and Structure in Novice Software Development Teams. Pittsburgh, PA: Carnegie Mellon University.