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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.

Copyright © 2016 by ASME
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References

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. 5

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

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Fig. 6

Screenshots of a typical simulation run for the with wall task

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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. 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. 9

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

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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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