Research Papers

Design of Complex Engineered Systems Using Multi-Agent Coordination

[+] Author and Article Information
Nicolás F. Soria Zurita

School of Mechanical, Industrial
and Manufacturing Engineering,
Oregon State University,
Corvallis, OR 97331;
Colegio de Ciencias e Ingeniería,
Universidad San Francisco de Quito,
Quito EC 170157, Ecuador
e-mail: soriazun@oregonstate.edu

Mitchell K. Colby

School of Mechanical, Industrial
and Manufacturing Engineering,
Oregon State University,
Corvallis, OR 97331
e-mail: colbym@engr.orst.edu

Irem Y. Tumer

School of Mechanical, Industrial
and Manufacturing Engineering,
Oregon State University,
Corvallis, OR 97331
e-mail: irem.tumer@oregonstate.edu

Christopher Hoyle

School of Mechanical, Industrial
and Manufacturing Engineering,
Oregon State University,
Corvallis, OR 97331
e-mail: chris.hoyle@oregonstate.edu

Kagan Tumer

School of Mechanical, Industrial
and Manufacturing Engineering,
Oregon State University,
Corvallis, OR 97331
e-mail: kagan.tumer@oregonstate.edu

1Corresponding author.

Contributed by the Computer-Aided Product Development Committee of ASME for publication in the JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING. Manuscript received October 5, 2016; final manuscript received October 2, 2017; published online November 28, 2017. Assoc. Editor: Yong Chen.

J. Comput. Inf. Sci. Eng 18(1), 011003 (Nov 28, 2017) (13 pages) Paper No: JCISE-16-2099; doi: 10.1115/1.4038158 History: Received October 05, 2016; Revised October 02, 2017

In complex engineering systems, complexity may arise by design, or as a by-product of the system's operation. In either case, the cause of complexity is the same: the unpredictable manner in which interactions among components modify system behavior. Traditionally, two different approaches are used to handle such complexity: (i) a centralized design approach where the impacts of all potential system states and behaviors resulting from design decisions must be accurately modeled and (ii) an approach based on externally legislating design decisions, which avoid such difficulties, but at the cost of expensive external mechanisms to determine trade-offs among competing design decisions. Our approach is a hybrid of the two approaches, providing a method in which decisions can be reconciled without the need for either detailed interaction models or external mechanisms. A key insight of this approach is that complex system design, undertaken with respect to a variety of design objectives, is fundamentally similar to the multi-agent coordination problem, where component decisions and their interactions lead to global behavior. The results of this paper demonstrate that a team of autonomous agents using a cooperative coevolutionary algorithm (CCEA) can effectively design a complex engineered system. This paper uses a system model of a Formula SAE racing vehicle to illustrate and simulate the methods and potential results. By designing complex systems with a multi-agent coordination approach, a design methodology can be developed to reduce design uncertainty and provide mechanisms through which the system level impact of decisions can be estimated without explicitly modeling such interactions.

Copyright © 2018 by ASME
Your Session has timed out. Please sign back in to continue.


Sullivan, M. J. , Mullins, B. , Bowman, R. C. , Franco, Q. C. , Lea, M. B. , Best, D. B. , Denomme, T. J. , Fairbairn, B. , Gallegos, A. , and Graveline, W. R. , 2008, “Defense Acquisitions: Assessments of Selected Weapon Programs,” U.S. Government Accountability Office, Washington, DC, Technical Report No. GAO-08-467 SP.
Collopy, P. , and Horton, R. , 2002, “Value Modeling for Technology Evaluation,” AIAA Paper No. 2002-3622.
Brown, O. , and Erenko, P. , 2008, “Application of Value-Centric Design to Space Architectures: The Case of Fractionated Spacecraft,” AIAA Paper No. 2008-7869.
Collopy, P. , 2001, “Economic-Based Distributed Optimal Design,” AIAA Paper No. 2001-4675.
Hazelrigg, G. A. , 1998, “ A Framework for Decision-Based Engineering Design,” ASME J. Mech. Des., 4(120), pp. 653–658.
Tumer, K. , and Agogino, A. K. , 2009, “ Multiagent Learning for Black Box System Reward Functions,” Adv. Complex Syst., 12(5), pp. 475–492.
Tiller, M. , 2001, Introduction to Physical Modeling With Modelica, Vol. 615, Springer, Boston, MA. [CrossRef]
Summers, J. D. , and Shah, J. J. , 2010, “ Mechanical Engineering Design Complexity Metrics: Size, Coupling, and Solvability,” ASME J. Mech. Des., 132(2), p. 021004.
ADP Team, 2001, “Advanced Projects Design Team: Assessment of ESA's World Space Observatory Proposal,” Assessment Report, Jet Propulsion Laboratory (JPL), Pasadena, CA, Technical Report No. CL#01-1168. http://neutrino.aquaphoenix.com/un-esa/JPL_final.pdf
Schuman, T. , de Weck, O. L. , and Sobieski, J. , 2005, “Integrated System-Level Optimization for Concurrent Engineering With Parametric Subsystem Modeling,” AIAA Paper No. 2005-2199.
Mark, G. , 2002, “ Extreme Collaboration,” Commun. ACM, 45(6), pp. 89–93. [CrossRef]
Chachere, J. , Kunz, J. , and Levitt, R. , 2004, “Observation, Theory, and Simulation of Integrated Concurrent Engineering: Grounded Theoretical Factors That Enable Radical Project Acceleration,” Center for Integrated Facility Engineering (CIFE), Stanford, CA, Report No. WP087. https://cife.stanford.edu/node/197
Garcia, A. C. B. , Kunz, J. , Ekstrom, M. , and Kiviniemi, A. , 2004, “Building a Project Ontology With Extreme Collaboration and Virtual Design and Construction,” Center for Integrated Facility Engineering (CIFE), Stanford, CA, CIFE Technical Report No. #152 https://cife.stanford.edu/node/235.
Wall, S. D., 1998, “ ICE Heats up Design Productivity,” Jet Propulsion Laboratory, Pasadena, CA, Technical Report https://trs.jpl.nasa.gov/handle/2014/19377.
Chen, W. , Wiecek, M. , and Zhang, J. , 1999, “ Quality Utility a Compromise Programming Approach to Robust Design,” ASME J. Mech. Des., 121(2), pp. 179–187. [CrossRef]
Huang, H. Z. , Wu, W. D. , and Liu, C. S. , 2005, “ A Coordination Method for Fuzzy Multi-Objective Optimization of System Reliability,” J. Intell. Fuzzy Syst., 16(3), pp. 213–220. https://content.iospress.com/articles/journal-of-intelligent-and-fuzzy-systems/ifs00262
Tappeta, R. , and Renaud, J. , 2001, “ Interactive Multiobjective Optimization Design Strategy for Decision Based Design,” ASME J. Mech. Des., 123(2), pp. 205–215. [CrossRef]
Wu, J. , and Azarm, S. , 2001, “ Metrics for Quality Assessment of a Multiobjective Design Optimization Solution Set,” ASME J. Mech. Des., 123(1), pp. 18–25. [CrossRef]
Sawaragi, Y. , Nakayama, H. , and Tanino, T. , 1985, Theory of Multiobjective Optimization, Vol. 176, Academic Press, Orlando, FL.
Steuer, R. , 1989, Multiple Criteria Optimization: Theory, Computation, and Application, Krieger, Malabar, FL.
Pareto, V. , 1971, Manual of Political Economy, A. S. Schwier , A. N. Page , and A. M. Kelley , eds., Augustus M. Kelley Publishers, New York.
Coello, C. A. , 1999, “ A Comprehensive Survey of Evolutionary-Based Multiobjective Optimization Techniques,” Knowl. Inf. Syst., 1(3), pp. 269–308. [CrossRef]
Das, I. , and Dennis, J. , 1998, “ Normal-Boundary Intersection: A New Method for Generating the Pareto Surface in Nonlinear Multicriteria Optimization Problems,” SIAM J. Optim., 8(3), pp. 631–657. [CrossRef]
Mattson, C. , and Messac, A. , 2005, “ Pareto Frontier Based Concept Selection Under Uncertainty, With Visualization,” Optim. Eng., 6(1), pp. 85–115. [CrossRef]
Horn, J. , Nafpliotis, N. , and Goldberg, D. , 1993, “Multiobjective Optimization Using the Niched Pareto Genetic Algorithm,” Illinois Genetic Algorithms Laboratory, Urbana, IL, IlliGAL Report No. 93005. http://citeseerx.ist.psu.edu/viewdoc/download?doi=
Braun, R. , and Gage, P. , 1996, “Implementation and Performance Issues in Collaborative Optimization,” AIAA Paper No. 96-4017.
Sobieski, I. P., and Kroo, I. M., 1993, “ Collaborative Optimization Using Response Surface Estimation,” AIAA J., 38(10), pp. 1931–1938.
Sobieszczanski-Sobieski, J. , Agte, J. S. , and Sandusky, R. R. , Jr., 1998, “Bilevel Integrated System Synthesis (BLISS),” NASA Langley Research Center, Hampton, VA, Technical Report No. NASA/TM-1998-208715 https://ntrs.nasa.gov/archive/nasa/casi.ntrs.nasa.gov/19980234657.pdf.
Sobieszczanski-Sobieski, J. , Emiley, M. , Agte, J. S. , and Sandusky, R. R. , Jr., 2000, “ Advancement of Bi-Level Integrated System Synthesis (BLISS),” AIAA Paper No. 2000-0421.
Sobieszczanski-Sobieski, J. , Altus, T. , Phillips, M. , and Sandusky, R. , 2003, “ Bilevel Integrated System Synthesis for Concurrent and Distributed Processing,” AIAA J., 41(915), pp. 1996–2003. [CrossRef]
Stone, P. , and Veloso, M. , 2000, “ Multiagent Systems: A Survey From a Machine Learning Perspective,” Auton. Robots, 8(3), pp. 345–383.
Busoniu, L. , Babuska, R. , and Schutter, B. D. , 2008, “ A Comprehensive Survey of Multiagent Reinforcement Learning,” IEEE Trans. Syst. Man Cybern., Part C, 38(2), p. 156. [CrossRef]
Agogino, A. K. , and Tumer, K. , 2004, “ Unifying Temporal and Structural Credit Assignment Problems,” Third International Joint Conference on Autonomous Agents and Multi-Agent Systems (AAMAS), New York, July 19–23, pp. 980–987. http://ieeexplore.ieee.org/document/1373617/
Stone, P. , 1998, Layered Learning in Multi-Agent Systems: A Winning Approach to Robotic Soccer, MIT Press, Cambridge, MA.
Paquet, S. , and Tobin, L. , 2005, “ An Online POMDP Algorithm for Complex Multiagent Environments,” Fourth International Joint Conference on Autonomous Agents and Multiagent Systems Conference (AAMAS), Utrecht, The Netherlands, July 25–29.
Babes, M. , de Cote, E. M. , and Littman, M. L. , 2008, “ Social Reward Shaping in the Prisoner's Dilemma,” Seventh International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS), Estoril, Portugal, May 12–16, pp. 1389–1392. https://dl.acm.org/citation.cfm?id=1402880
Buffet, O. , Dutech, A. , and Charpillet, F. , 2007, “ Shaping Multi-Agent Systems With Gradient Reinforcement Learning,” Auton. Agents Multi-Agent Syst., 15(2), pp. 197–220.
Abdallah, S. , and Lesser, V. , 2007, “ Multiagent Reinforcement Learning and Self-Organization in a Network of Agents,” Sixth International Joint Conference on Autonomous Agents and Multi-Agent Systems (AAMAS), Honolulu, HI, May 14–18.
Zhang, C. , Abdallah, S. , and Lesser, V. , 2009, “ Integrating Organizational Control Into Multi-Agent Learning,” Eighth International Conference on Autonomous Agents and Multiagent Systems (AAMAS), Budapest, Hungary, May 10–15. https://dl.acm.org/citation.cfm?id=1558109.1558116
Tumer, K. , and Wolpert, D. , 2004, Collectives and the Design of Complex Systems, Springer, New York. [CrossRef]
Agogino, A. K. , and Tumer, K. , 2008, “ Efficient Evaluation Functions for Evolving Coordination,” Evol. Comput., 16(2), pp. 257–288.
Agogino, A. K. , and Tumer, K. , 2012, “ A Multiagent Approach to Managing Air Traffic Flow,” Auton. Agents Multi-Agent Syst., 24(1), pp. 1–25.
Agogino, A. K. , and Tumer, K. , 2007, “ Distributed Agent-Based Air Traffic Flow Management,” Sixth International Joint Conference on Autonomous Agents and Multi-Agent Systems (AAMAS), Honolulu, HI, May 14–18.
Tumer, K. , and Khani, N. , 2009, “ Learning From Actions Not Taken in Multiagent Systems,” Adv. Complex Syst., 12(4–5), pp. 455–473. http://citeseerx.ist.psu.edu/viewdoc/download?doi=
Fogel, D. , 1994, “ An Introduction to Simulated Evolutionary Optimization,” IEEE Trans. Neural Networks, 5(1), pp. 3–14. [CrossRef]
Panait, L. , Luke, S. , and Wiegand, R. P. , 2006, “ Biasing Coevolutionary Search for Optimal Multiagent Behaviors,” IEEE Trans. Evol. Comput., 10(6), pp. 629–645. [CrossRef]
Potter, M. A. , and De Jong, K. A. , 1995, “ Evolving Neural Networks With Collaborative Species,” Computer Simulation Conference, Ottawa, ON, Canada, July 24–26. http://citeseerx.ist.psu.edu/viewdoc/download?doi=
Wiegand, R. P. , Jong, K. A. D. , and Liles, W. C. , 2002, “Modeling Variation in Cooperative Coevolution Using Evolutionary Game Theory,” Foundations of Genetic Algorithms, Morgan Kaufmann, Burlington, MA, pp. 203–220.
Colby, M. , and Tumer, K. , 2017, “ Fitness Function Shaping in Multiagent Cooperative Coevolutionary Algorithms,” Auton. Agents Multi-Agent Syst., 31(2), pp. 179–206. [CrossRef]
Colby, M. K. , Kharaghani, S. , HolmesParker, C. , and Tumer, K. , 2015, “ Counterfactual Exploration for Improving Multiagent Learning,” International Conference on Autonomous Agents and Multiagent Systems (AAMAS), Istanbul, Turkey, May 4–8, pp. 171–179. https://dl.acm.org/citation.cfm?id=2772904
HolmesParker, C. , Agogino, A. K. , and Tumer, K. , 2016, “ Combining Reward Shaping and Hierarchies for Scaling to Large Multiagent Systems,” Knowl. Eng. Rev., 31(1), pp. 3–18. [CrossRef]
Turner, K. , 2006, “ Designing Agent Utilities for Coordinated, Scalable and Robust Multi-Agent Systems,” Coordination of Large-Scale Multiagent Systems, P. Scerri , R. Vincent , and R. Mailler , eds., Springer, New York, pp. 173–188. [CrossRef]
Colby, M. , and Tumer, K. , 2015, “ An Evolutionary Game Theoretic Analysis of Difference Evaluation Functions,” Annual Conference on Genetic and Evolutionary Computation (GECCO), Madrid, Spain, July 11–15, pp. 1391–1398.
SAE, 2015, “ SAE Collegiate Design Series,” SAE International, Warrendale, PA, accessed Sept. 18, 2015, http://students.sae.org/cds/formulaseries/rules/
Tumer, K. , and Wolpert, D. , 2004, “ A Survey of Collectives,” Collectives and the Design of Complex Systems, K. Tumer and D. Wolpert , eds., Springer, New York, pp. 1–42. [CrossRef]
NASA, 2015, “Shape Effects on Drag,” National Aeronautics and Space Administration, Washington, DC, accessed May 26, 2016, https://www.grc.nasa.gov/www/k-12/airplane/shaped.html
GFR, 2016, “ Global Formula Racing,” Global Formula Racing, Baden-Württemberg, Germany, accessed Sept. 10, 2015, http://www.global-formula-racing.com


Grahic Jump Location
Fig. 2

Design process for agents

Grahic Jump Location
Fig. 3

Racing vehicle model: 1—rear wing, 2—front wing, 3—side wing, 4—rear tire, 5—front tire, 6—engine, 7—cabin, 8—impact attenuator, 9—brake system, 10—rear suspension, and 11—front suspension

Grahic Jump Location
Fig. 4

Cooperative coevolutionary algorithm methodology

Grahic Jump Location
Fig. 5

System performance of difference evaluations Di(z) and global evaluations G(z)



Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In