Swarm intelligence: Literature overview

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Reference

Liu, Y., Passino, K.M.: Swarm intelligence: Literature overview, 2000

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Abstract

1 Swarms A long time ago, people discovered the variety of the interesting insect or animal behaviors in the nature. A ock of birds sweeps across the sky. A group of ants forages for food. A school of sh swims, turns, ees together, etc. 1]. We call this kind of aggregate motion \swarm behavior." Recently biologists, and computer scientists in the eld of \arti cial life" have studied how to model biological swarms to understand how such \social animals" interact, achieve goals, and evolve. Moreover, engineers are increasingly interested in this kind of swarm behavior since the resulting \swarm intel- ligence" can be applied in optimization (e.g. in telecommunicate systems) 2], robotics 3, 4], tra c patterns in transportation systems, and military applications 5].

A high-level view of a swarm suggests that the N agents in the swarm are cooperating to achieve some proposeful behavior and achieve some goal. This apparent \collective intelligence" seems to emerge from what are often large groups of relatively simple agents. The agents use simple local rules to govern their actions and via the interactions of the entire group, the swarm achieves its objectives. A type of \self-organization" emerges from the collection of actions of the group.

Swarm intelligence is the emergent collective intelligence of groups of simple autonomous agents. Here, an autonomous agent is a subsystem that interacts with its environment, which probably consists of other agents, but acts relatively independently from all other agents. The autonomous agent does not follow commands from a leader, or some global plan 6]. For ex- ample, for a bird to participate in a ock, it only adjusts its movements to coordinate with the movements of its ock mates, typically its \neighbors" that are close to it in the ock. A bird in a ock simply tries to stay close to its neighbors, but avoid collisions with them. Each bird does not take commands from any leader bird since there is no lead bird. Any bird can y in the front, center and back of the swarm. Swarm behavior helps birds take advantage of several things including protection from predators (especially for birds in the middle of the ock), and searching for food (essentially each bird is exploiting the eyes of every other bird).

Extended Abstract

Bibtex

Used References

References 1] E. Shaw, \The schooling of shes," Sci. Am., vol. 206, pp. 128{138, 1962.

2] E. Bonabeau, M. Dorigo, and G. Theraulaz, Swarm Intelligence: From Natural to Arti cial Systems. NY: Oxford Univ. Press, 1999.

3] R. Arkin, Behavior-Based Robotics. Cambridge, MA: MIT Press, 1998.

4] G. Beni and J. Wang, \Swarm intelligence in cellular robotics systems," in Proceeding of NATO Advanced Workshop on Robots and Biological System, 1989.

5] M. Pachter and P. Chandler, \Challenges of autonomous control," IEEE Control Systems Magazine, pp. 92{97, April 1998.

6] G. Flake, The Computational Beauty of Nature. Cambridge, MA: MIT Press, 1999.

7] C. Reynolds, \Flocks, herds, and schools: A distributed behavioral model," Comp. Graph, vol. 21, no. 4, pp. 25{34, 1987.

8] M. Resnick, Turtles, Termites, and Tra c Jams: Explorations in Mas- sively Parallel Microworlds. Cambridge, MA: MIT Press, 1994.

9] D. Terzopoulos, X. Tu, and R. Grzeszczuk, \Arti cial shes with au- tonomous locomotion, perception, behavior, and learning in a simulated physical world," in Arti cial Life I, p. 327, MIT Press, 1994.

10] M. Millonas, \Swarms, phase transitions, and collective intelligence," in Arti cial Life III, Addison-Wesley, 1994.

11] K. Sims, \Evolving 3d morphology and behavior by competition," in Arti cial Life I, p. 353, MIT Press, 1994.

12] G. Dudek and et al., \A taxonomy for swarm robots," in IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, (Yokohama, Japan), July 1993.

13] S. Hackwood and S. Beni, \Self-organization of sensors for swarm in- telligence," in IEEE Int. Conf. on Robotics and Automation, (Nice, France), pp. 819{829, May 1992.

14] R. Brooks, \Intelligence without reason," tech. rep., Arti cial Intelli- gence Memo. No. 1293, 1991.

15] T. Ueyama, T. Fukuda, and F. Arai, \Con guration of communication structure for distributed intelligent robot system," in Proc. IEEE Int. Conf. on Robotics and Automation, pp. 807{812, 1992.

16] M. Mataric, \Minimizing complexity in controlling a mobile robot population," in IEEE Int. Conf. on Robotics and Automation, (Nice, France), May 1992.

17] T. Fukuda, T. Ueyama, and T. Sugiura, \Self-organization and swarm intelligence in the society of robot being," in Proceedings of the 2nd In- ternational Symposium on Measurement and Control in Robotics, 1992.

18] T. Fukuda, G. Iritani, T. Ueyama, and F. Arai, \Optimization of group behavior on cellular robotic system in dynamic environment," in Pro- ceedings of the 1994 IEEE International Conference on Robotics and Automation, pp. 1027{1032, 1994.

19] T. Fukuda, D. Funato, K. Sekiyam, and F. Arai, \Evaluation on ex- ibility of swarm intelligent system," in Proceedings of the 1998 IEEE International Conference on Robotics and Automation, pp. 3210{3215, 1998.

20] A. Mogilner and L. Edelstein-Keshet, \A non-local model for a swarm," Journal of Mathematical Biology, vol. 38, pp. 534{570, 1999.

21] K. Jin, P. Liang, and G. Beni, \Stability of synchronized distributed control of discrete swarm structures," in IEEE International Conference on Robotics and Automation, pp. 1033{1038, 1994.

22] G. Beni and P. Liang, \Pattern recon guration in swarms{convergence of a distributed asynchronous and bounded iterative algorithm," IEEE Trans. on Robotics and Automation, vol. 12, June 1996.

23] K. Passino and K. Burgess, Stability Analysis of Discrete Event Sys- tems. New York: John Wiley and Sons Pub., 1998.


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