Using organization knowledge to improve routing performance in wireless multi-agent networks

ABSTRACT

Multi-agent systems benefit greatly from an organization design that guides agents in determining when to communicate, how often, with whom, with what priority, and so on. However, this same organization knowledge is not utilized by general-purpose wireless network routing algorithms normally used to support agent communication. We show that incorporating organization knowledge (otherwise available only to the application layer) in the network-layer routing algorithm increases bandwidth available at the application layer by as much as 35 percent. This increased bandwidth is especially important in communication-intensive application settings, such as agent-based sensor networks, where node failures and link dynamics make providing sufficient inter-agent communication especially challenging.

REFERENCES

• 1: J. Boyan and M. Littman. Packet Routing in dynamically changing networks: A reinforcement learning approach. Cowan, J. D.; Tesauro, G.; and Alspector, J., eds., Advances in Neural Information Processing Systems, 1994.
• 2: T. Clausen , P. Jacquet, Optimized Link State Routing Protocol (OLSR), RFC Editor, 2003
• 3: D. Corkill, D. Holzhauer, and W. Koziarz. Turn Off Your Radios! Environmental Monitoring Using Power-Constrained Sensor Agents. First International Workshop on Agent Technology for Sensor Networks (ATSN-07), 2007.
• 4: D. Corkill and V. Lesser. The use of meta-level control for coordination in a distributed problem solving network. Proceedings of the Eighth International Joint Conference on Artificial Intelligence, pages 748--756, August 1983.
• 5: Bryan Horling , Victor Lesser, A survey of multi-agent organizational paradigms, The Knowledge Engineering Review, v.19 n.4, p.281-316, December 2004 [doi>10.1017/S0269888905000317]
• 6: Srinivasan Keshav, REAL: A Network Simulator, University of California at Berkeley, Berkeley, CA, 1988
• 7: Michael Krainin , Bo An , Victor Lesser, An Application of Automated Negotiation to Distributed Task Allocation, Proceedings of the 2007 IEEE/WIC/ACM International Conference on Intelligent Agent Technology, p.138-145, November 02-05, 2007 [doi>10.1109/IAT.2007.29]
• 8: Shailesh Kumar, Confidence based Dual Reinforcement Q-Routing: an On-line Adaptive NetworkRouting Algorithm, University of Texas at Austin, Austin, TX, 1998
• 9: J. McQuillan and D. Walden. The ARPANET Design Decisions. Computer Networks, 1, August 1992.
• 10: R. Onishi, S. Yamaguchi, H. Morino, H. Aida, and T. Saito. A multi-agent system for dynamic network routing. IEICE Transactions of Communications, 84-B(10):2721--2728, 2001.
• 11: Charles E. Perkins , Pravin Bhagwat, Highly dynamic Destination-Sequenced Distance-Vector routing (DSDV) for mobile computers, ACM SIGCOMM Computer Communication Review, v.24 n.4, p.234-244, Oct. 1994
• 12: Charles E. Perkins , Elizabeth M. Royer, Ad-hoc On-Demand Distance Vector Routing, Proceedings of the Second IEEE Workshop on Mobile Computer Systems and Applications, p.90, February 25-26, 1999
• 13: C. Watkins and P. Dayan. Q-learning. Machine Learning, 8:279--292, 1989.

INDEX TERMS

Primary Classification:
  C. Computer Systems Organization
  C.2 COMPUTER-COMMUNICATION NETWORKS
      C.2.2 Network Protocols

Additional Classification:
  C. Computer Systems Organization
  C.2 COMPUTER-COMMUNICATION NETWORKS
      C.2.4 Distributed Systems

  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
        I.2.11 Distributed Artificial Intelligence

General Terms:
Algorithms, Design, Experimentation, Verification

Keywords:
agents, bandwidth, communication, multi-agent sensor networks, organization design, wireless routing