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Research Papers: Design Theory and Methodology

Effect of Social Structuring in Self-Organizing Systems

[+] Author and Article Information
Newsha Khani

Department of Aerospace
and Mechanical Engineering,
University of Southern California,
3650 McClintock Avenue, OHE430,
Los Angeles, CA 90089-1453
e-mail: nkhani@usc.edu

James Humann

Department of Aerospace
and Mechanical Engineering,
University of Southern California,
3650 McClintock Avenue, OHE430,
Los Angeles, CA 90089-1453
e-mail: humann@usc.edu

Yan Jin

Department of Aerospace
and Mechanical Engineering,
University of Southern California,
3650 McClintock Avenue, OHE430,
Los Angeles, CA 90089-1453
e-mail: yjin@usc.edu

1Corresponding author.

Contributed by the Design Theory and Methodology Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received March 12, 2015; final manuscript received November 20, 2015; published online February 23, 2016. Assoc. Editor: Andy Dong.

J. Mech. Des 138(4), 041101 (Feb 23, 2016) (11 pages) Paper No: MD-15-1213; doi: 10.1115/1.4032265 History: Received March 12, 2015; Revised November 20, 2015

Dealing with unforeseeable changing situations, often seen in exploratory and hazardous task domains, requires systems that can adapt to changing tasks and varying environments. The challenge for engineering design researchers and practitioners is how to design such adaptive systems. Taking advantage of the flexibility of multi-agent systems, a self-organizing systems approach has been proposed, in which mechanical cells or agents organize themselves as the environment and tasks change based on a set of predefined rules. To enable self-organizing systems to perform more realistic tasks, a two-field framework is introduced to capture task complexity and agent behaviors, and a rule-based social structuring mechanism is proposed to facilitate self-organizing for better performance. Computer simulation-based case studies were carried out to investigate how social structuring among agents, together with the size of agent population, can influence self-organizing system performance in the face of increasing task complexity. The simulation results provide design insights into task-driven social structures and their effect on the behavior and performance of self-organizing systems.

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References

Zouein, G. , Chen, C. , and Jin, Y. , 2010, “ Create Adaptive Systems Through ‘DNA’ Guided Cellular Formation,” 1st International Conference on Design Creativity, pp. 149–156.
Chiang, W. , and Jin, Y. , 2011, “ Toward a Meta-Model of Behavioral Interaction for Designing Complex Adaptive Systems,” ASME Paper No. DETC2011-48821.
Jin, Y. , and Chen, C. , 2014, “ Field Based Behavior Regulation for Self-Organization in Cellular Systems,” Design Computing and Cognition '12, Springer, Dordrecht, The Netherlands.
Chen, C. , and Jin, Y. , 2011, “ A Behavior Based Approach to Cellular Self-Organizing Systems Design,” ASME Paper No. DETC2011-48833.
Humann, J. , and Jin, Y. , 2013, “ Evolutionary Design of Cellular Self-Organizing Systems,” ASME Paper No. DETC2013-12485.
Jin, Y. , and Chen, C. , 2014, “ Cellular Self-Organizing Systems: A Field-Based Behavior Regulation Approach,” Artif. Intell. Eng. Des. Anal. Manuf., 28(2), pp. 115–128. [CrossRef]
Simon, H. A. , 1962, “ The Architecture of Complexity,” Proc. Am. Philos. Soc., 106(6), pp. 467–482.
Williams, E. L. , 1981, Thermodynamics and the Development of Order, Creation Research Society Books, Norcross, GA.
Ferguson, S. , and Lewis, K. , 2006, “ Effective Development of Reconfigurable Systems Using Linear State-Feedback Control,” AIAA J., 44(4), pp. 868–878. [CrossRef]
Martin, M. V. , and Ishii, K. , 1997, “ Design for Variety: Development of Complexity Indices and Design Charts,” ASME Paper No. DETC97/DFM-4359.
Rus, D. , and Vona, M. , 1999, “ Self-Reconfiguration Planning With Compressible Unit Modules,” IEEE International Conference on Robotics and Automation, Detroit, MI, Vol. 4, pp. 2513–2520.
Rus, D. , and Vona, M. , 2000, “ A Physical Implementation of the Self-Reconfiguring Crystalline Robot,” IEEE International Conference on Robotics and Automation, ICRA’00, Vol. 2, pp. 1726–1733.
Rus, D. , and Vona, M. , 2001, “ Crystalline Robots: Self-Reconfiguration With Compressible Unit Modules,” Auton. Rob., 10(1), pp. 107–124. [CrossRef]
Fukuda, T. , and Nakagawa, S. , 1987, “ A Dynamically Reconfigurable Robotic System (Concept of a System and Optimal Configurations),” Robotics and IECON’87 Conferences, pp. 588–595.
Unsal, C. , Kiliccote, H. , and Khosla, P. K. , 1999, “ I (CES)-Cubes: A Modular Self-Reconfigurable Bipartite Robotic System,” Proc. SPIE, 3839, pp. 258–269.
Prevas, K. C. , Unsal, C. , Efe, M. O. , and Khosla, P. K. , 2002, “ A Hierarchical Motion Planning Strategy for a Uniform Self-Reconfigurable Modular Robotic System,” IEEE International Conference on Robotics and Automation, ICRA’02, Vol. 1, pp. 787–792.
Yim, M. , 1993, “ A Reconfigurable Modular Robot With Many Modes of Locomotion,” International Conference on Advanced Mechatronics, pp. 283–288.
Yim, M. , Zhang, Y. , and Duff, D. , 2002, “ Modular Robots,” IEEE Spectrum, 39(2), pp. 30–34. [CrossRef]
Shen, W. M. , Krivokon, M. , Chiu, H. , Everist, J. , Rubenstein, M. , and Venkatesh, J. , 2006, “ Multimode Locomotion Via SuperBot Reconfigurable Robots,” Auton. Rob., 20(2), pp. 165–177. [CrossRef]
Arkin, R. C. , and Balch, T. , 1998, “ Cooperative Multiagent Robotic Systems,” Artificial Intelligence and Mobile Robots, MIT/AAAI Press, Cambridge, MA.
Brooks, R. , 1986, “ A Robust Layered Control System for a Mobile Robot,” IEEE J. Rob. Autom., 2(1), pp. 14–23. [CrossRef]
Mataric, M. J. , 1997, “ Behaviour-Based Control: Examples From Navigation, Learning, and Group Behaviour,” J. Exp. Theor. Artif. Intell., 9(2–3), pp. 323–336. [CrossRef]
Parker, L. E. , 1998, “ ALLIANCE: An Architecture for Fault Tolerant Multirobot Cooperation,” IEEE Trans. Rob. Autom., 14(2), pp. 220–240. [CrossRef]
Horling, B. , and Lesser, V. , 2004, “ A Survey of Multi-Agent Organizational Paradigms,” Knowl. Eng. Rev., 19(4), pp. 281–316. [CrossRef]
Thompson, J. D. , 1967, Organizations in Action: Social Science Bases of Administrative Theory, Transaction Publishers, New Brunswick, NJ.
Galbraith, J. R. , 1977, Organization Design, Addison-Wesley, Reading, MA.
Scott, W. , 1992, Richard: Organizations-Rational, Natural, and Open Systems, Prentice Hall, New York.
Durfee, E. H. , Lesser, V. R. , and Corkill, D. D. , 1987, “ Coherent Cooperation Among Communicating Problem Solvers,” IEEE Trans. Comput., 100(11), pp. 1275–1291. [CrossRef]
Horling, B. , Mailler, R. , and Lesser, V. , 2004, “ A Case Study of Organizational Effects in a Distributed Sensor Network,” IEEE/WIC/ACM International Conference on Intelligent Agent Technology, IAT 2004, Sept. 20–24, pp. 51–57.
Matson, E. , and DeLoach, S. , 2003, “ Using Dynamic Capability Evaluation to Organize a Team of Cooperative, Autonomous Robots,” International Conference on Artificial Intelligence (IC-AI '03), Vol. 2, pp. 744–749.
Barber, K. S. , Goel, A. , and Martin, C. E. , 2000, “ Dynamic Adaptive Autonomy in Multi-Agent Systems,” J. Exp. Theor. Artif. Intell., 12(2), pp. 129–147. [CrossRef]
So, Y. , and Durfee, E. H. , 1998, Designing Organizations for Computational Agents, Simulating Organizations: Computational Models of Institutions and Groups, MIT Press, Cambridge, MA.
Brooks, C. H. , and Durfee, E. H. , 2003, “ Congregation Formation in Multiagent Systems,” Auton. Agents Multi-Agent Syst., 7(1), pp. 145–170. [CrossRef]
Lesser, V. R. , 1998, “ Reflections on the Nature of Multi-Agent Coordination and Its Implications for an Agent Architecture,” Auton. Agents Multi-Agent Syst., 1(1), pp. 89–111. [CrossRef]
Durfee, E. H. , 2001, “ Scaling up Agent Coordination Strategies,” Computer, 34(7), pp. 39–46. [CrossRef]
Humann, J. , and Madni, A. M. , 2014, “ Integrated Agent-Based Modeling and Optimization in Complex Systems Analysis,” Procedia Comput. Sci., 28, pp. 818–827. [CrossRef]
Humann, J. , Khani, N. , and Jin, Y. , 2014, “ Evolutionary Computational Synthesis of Self-Organizing Systems,” Art. Intell. Eng. Design, Anal. Manuf., 28(3), pp. 259–275. [CrossRef]
Ashby, W. R. , 1956, An Introduction to Cybernetics, Chapman & Hail, London.
Huberman, B. A. , and Hogg, T. , 1986, “ Complexity and Adaptation,” Phys. Nonlinear Phenom., 22(1), pp. 376–384. [CrossRef]
Wood, R. E. , 1986, “ Task Complexity: Definition of the Construct,” Organ. Behav. Hum. Decis. Processes, 37(1), pp. 60–82. [CrossRef]
Campbell, D. J. , 1988, “ Task Complexity: A Review and Analysis,” Acad. Manage. Rev., 13(1), pp. 40–52.
Gell-Mann, M. , 2002, “ What is Complexity?,” Complexity and Industrial Clusters, Physica-Verlag, Heidelberg, pp. 13–24.
Bonchev, D. D. , and Rouvray, D. H. , 1991, Chemical Graph Theory: Introduction and Fundamentals, Vol. 1, Abacus Press, New York.
Randi, M. , and Plav, D. , 2002, “ On the Concept of Molecular Complexity,” Croat. Chem. Acta, 75(1), pp. 107–116.
Bonchev, D. , 1983, Information Theoretic Indices for Characterization of Chemical Structures, Vol. 5, Research Studies Press, Chichester, UK.
Bonchev, D. , 2003, “ Shannon's Information and Complexity,” Complexity in Chemistry Introduction and Fundamental, D. Bonchev , and D. H. Rouvray , eds., Taylor & Francis, London, pp. 157–187.
Bonchev, D. , 2003, “ On the Complexity of Directed Biological Networks,” SAR QSAR Environ. Res., 14(3), pp. 199–214. [CrossRef] [PubMed]
Bonchev, D. D. , and Rouvray, D. , 2007, Complexity in Chemistry, Biology, and Ecology, Springer, New York.
Zhang, C. , Abdallah, S. , and Lesser, V. , 2008, “ Efficient Multi-Agent Reinforcement Learning Through Automated Supervision,” 7th International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS '08), Vol. 3, pp. 1365–1370.
Gershenson, C. , 2005, “ A General Methodology for Designing Self-Organizing Systems,” Preprint arXiv:Nlin0505009.
Jin, Y. , and Levitt, R. E. , 1996, “ The Virtual Design Team: A Computational Model of Project Organizations,” Comput. Math. Organ. Theory, 2(3), pp. 171–195. [CrossRef]
Wilensky, U. , 2001, “ Modeling Nature's Emergent Patterns With Multi-Agent Languages,” EuroLogo, pp. 43–52.

Figures

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Fig. 1

Interplay among social structuring, size of agent population, task complexity, and system performance

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Fig. 2

Hypothetical system complexity over order–disorder spectrum (adapted from Huberman and Hogg [39])

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Fig. 3

Box-moving task used in case studies

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Fig. 4

Possible conflicts of agents i and j and box neighborhood: (a) moving force conflict, (b) rotation torque conflict, and (c) box neighborhood

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Fig. 9

Social complexity during the process of moving the box toward the goal with SRBR strategy and 12 agents

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Fig. 8

Time duration comparison for various social rule adoption policies for the with wall task with varying number of agents

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Fig. 7

Total effort comparison for various social rule adoption policies for the with wall task with varying number of agents

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Fig. 6

Screenshots of a typical simulation run for the with wall task

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Fig. 5

Experiment design with three-independent variables and three-dependent variables

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Fig. 10

Success rate comparison for various social rule adoption policies for the with wall + one obstacle task with varying number of agents

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Fig. 11

Total effort comparison for various social rule adoption policies for the with wall + one obstacle task with varying number of agents

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Fig. 12

Duration time comparison for various social rule adoption policies for the with wall + one obstacle task with varying number of agents

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Fig. 13

Success rate comparison for various social rule adoption policies for the with wall + two obstacles task with varying number of agents

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Fig. 14

Total effort comparison for various social rule adoption policies for the with wall + two obstacles task with varying number of agents

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Fig. 15

Duration time comparison for various social rule adoption policies for the with wall + two obstacles task with varying number of agents

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