Distributed Mega-Scale Agent Management
in a parallel-programming
simulation and analysis environment:
injection, diffusion, guarded migration,
merger and distributed termination
Initial Draft: Related Works Section
Cherie Wasous
December 12, 2013
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Table of Contents
Related Works ............................................................................................................................................... 3
Probably Not Related Works < not in final thesis version > ......................................................................... 4
References ..................................................................................................................................................... 5
Appendix A. Glossary < not in final thesis version >.................................................................................. 7
Appendix B. Mendeley organizes related works & automates citation in Word ....................................... 7
Table of Figures
Figure 1. Snapshot of my Mendeley desktop ............................................................................................... 7
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Related Works
The platform used for evaluating management of mega-agents is described in [1]. Other related
platforms are described in [2] [3] http://www.dmason.org/ and [4]
http://repast.sourceforge.net/repast_hpc.php .
Wireless sensor networks are similar to a distributed mega-agent platform in that they both want
to minimize messages over the network, avoiding client/server computing, and instead
implement distributed diffusion and distributed aggregation. Mobile agent based directed
diffusion in wireless sensor networks is discussed in [5].
There are many articles concerning collision avoidance, especially involving robots. Path
planning to avoid collisions also gets much interest in the literature. A resolution of the
concurrent access problem is addressed in this paper, however it is not deterministic [6].
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Probably Not Related Works < not in final thesis version >
GAMA is a modeling and simulation development environment for building spatially explicit
agent-based simulations [7]. Uses Repast as the simulation library.
MASON: Multi-Agent Simulator of Neighborhoods… or Networks… or something…. [8].
Ant robots navigation methods based on real-time search, including going around obstacles [9].
Distributed coordination of agent schedule [10].
New local collision avoidance algorithm between multiple agents for real-time simulations [11].
Design algorithms not to minimize arithmetic operations, but to minimize communication both
within a local memory hierarchy and between processors [12].
Study of Information Diffusion over a Realistic Social Network Model [13].
An asynchronous decentralized prioritized planning algorithm for space-time cooperative path
finding problem [14].
Distributed management of agent schedules [10].
Co-ordination in multi-agent systems. Overviews various co-ordination techniques [15].
Two algorithms for two ants to rendezvous [16].
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Three books with background information [17] [18] [19].
References
[1]
T. Chuang and M. Fukuda, “A Parallel Multi-Agent Spatial Simulation Enviornment for
Cluster Systems,” 2013.
[2]
G. Cordasco, R. De Chiara, A. Mancuso, D. Mazzeo, V. Scarano, and C. Spagnuolo,
“Bringing together efficiency and effectiveness in distributed simulations: The experience
with D-Mason,” Simulation, vol. 89, no. 10, pp. 1236–1253, Jun. 2013.
[3]
V. Scarano, “D-MASON: a short tutorial.” 2013.
[4]
N. Collier and M. North, “Parallel agent-based simulation with Repast for High
Performance Computing,” Simulation, vol. 89, no. 10, pp. 1215–1235, Nov. 2012.
[5]
M. Chen, T. Kwon, Y. Yuan, Y. Choi, and V. C. Leung, “Mobile Agent-Based Directed
Diffusion in Wireless Sensor Networks,” EURASIP J. Adv. Signal Process., vol. 2007, no.
1, p. 13, 2007.
[6]
P. Nicolas and O. Simonin, “Intelligent Tiles : Putting Situated Multi-agents Models in
Real World,” 2000.
[7]
“GAMA code site.” [Online]. Available: http://code.google.com/p/gama-platform/.
[8]
“MASON home site.” [Online]. Available: http://cs.gmu.edu/~eclab/projects/mason/.
[9]
S. Koenig, B. Szymanski, and Y. Liu, “Efficient and inefficient ant coverage methods,”
Ann. Math. Artif. Intell., no. 31, pp. 41–76, 2001.
[10] L. Barbulescu, Z. B. Rubinstein, S. F. Smith, and T. L. Zimmerman, “Distributed
Coordination of Mobile Agent Teams : The Advantage of Planning Ahead,” in Proc. of
9th Int. Conf. on Au-tonomous Agents and Multiagent Systems (AAMAS 2010), 2010.
[11] S. J. Guy, J. Chhugani, C. Kim, N. Satish, M. Lin, D. Manocha, and P. Dubey,
“ClearPath: highly parallel collision avoidance for multi-agent simulation,” in
Proceedings of the 2009 ACM SIGGRAPH/Eurographics Symposium on Computer
Animation - SCA ’09, 2009, pp. 177–187.
[12] J. Demmel, M. Hoemmen, M. Mohiyuddin, and K. Yelick, “Avoiding communication in
sparse matrix computations,” 2008 IEEE Int. Symp. Parallel Distrib. Process., pp. 1–12,
Apr. 2008.
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[13] A. Apolloni, K. Channakeshava, L. Durbeck, M. Khan, C. Kuhlman, B. Lewis, and S.
Swarup, “A Study of Information Diffusion over a Realistic Social Network Model,” in
2009 International Conference on Computational Science and Engineering, 2009, pp.
675–682.
[14] P. Novák, “Asynchronous Decentralized Algorithm for Space-Time Cooperative
Pathfinding,” in Workshop Proceedings of the European Conference on Articial
Intelligence (ECAI 2012), 2012.
[15] H. S. Nwana, L. Lee, and N. R. Jennings, “Co-ordination in software agent systems,”
1996.
[16] A. Shiloni, A. Levy, A. Felner, and M. Kalech, “Ants Meeting Algorithms,” in Proc. of
9th Int. Conf. on Autonomous Agents and Multiagent Systems (AAMAS), 2010, pp. 567–
574.
[17] L. S. C. Lin, Principles of Parallel Programming. Addison-Wesley Computing, 2008, p.
338.
[18] G. Weiss, Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence.
The MIT Press, 1999, p. 619.
[19] G. T. E. Bonabeau, M. Dorigo, Swarm Intelligence From Natural to Artificial Systems.
Oxford University Press, 1999, p. 307.
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Appendix A. Glossary < not in final thesis version >
Agent-based Modeling (ABM) – < TBD: same as IBM? >
DAI – Distributed Artificial Intelligence
Individual-based Modeling (IBM) – each agent has own set of internal state variables affected by
its own history, and also allow spatial locality in the dynamics. Rule-based behavior of
individual agents. These models very suitable for distributed parallel computing.
MAS – Multi-Agent System
Appendix B. Mendeley organizes related works & automates citation in
Word
Figure 1. Snapshot of my Mendeley desktop
< email me cwasous@uw.edu if you would like to be invited to access these related works >
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