This chapter presents the design and evaluation of an ant-based approach to query routing in peer-to-peer networks. After pointing out how to employ the ant metaphor for query routing, we evaluate the impact of different settings for the configurable parameters present in ant algorithms on the performance values. In particular, the focus is on the effects of setting the ratio between ants exploiting the option currently known as the best one, and ants exploring the search space with the aim of finding improved options. We show that the exploitationexploration dilemma can be avoided by an adequate design of the exploring option.
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References
Gianni Di Caro, Frederick Ducatelle, and Luca Maria Gambardella, “AntHocNet: An Ant-based Hybrid Routing Algorithm for Mobile and Ad Hoc Networks,” in Proceedings of Parallel Problem Solving from Nature. September 2004, vol. 3242 of Lecture Notes in Computer Science, Springer.
Gianni Di Caro and Marco Dorigo, “AntNet: Distributed Stigmergy Control for Com-munications Networks,” Journal of Artificial Intelligence Research (JAIR), vol. 9, pp. 317-365, July 1998.
Sam Joseph and Takashige Hoshiai, “Decentralized Meta-Data Strategies: Effective Peer-to-Peer Search,” IEICE Transactions on Communications, vol. E86-B, no. 6, pp. 1740-1753, June 2003.
Elke Michlmayr, Arno Pany, and Sabine Graf, “Applying Ant-based Multi-Agent Systems to Query Routing in Distributed Environments,” in Proceedings of the 3rd IEEE Conference On Intelligent Systems (IEEE ISŠ06), September 2006.
Elke Michlmayr, “Ant Algorithms for Search in Unstructured Peer-to-Peer Networks,” in Proceedings of the Ph.D. Workshop, 22nd International Conference on Data Engineering (ICDE 2006), April 2006.
Marco Dorigo and Gianni Di Caro, New Ideas in Optimization, chapter The Ant Colony Optimization Meta-Heuristic, pp. 11-32, McGraw-Hill, 1999.
John H. Holland, Adaptation in Natural and Artificial Systems, MIT Press, 1992.
Stewart W. Wilson, “Explore/Exploit Strategies in Autonomy,” in From Animals to Ani-mats 4: Proceedings of the 4th International Conference on the Simulation of Adaptive Behavior, 1996, pp. 325-332.
Richard S. Sutton and Andrew G. Barto, Reinforcement Learning: An Introduction, MIT Press, 1998.
Yael Niv, Daphna Joel, Isaac Meilijson, and Eytan Ruppin, “Evolution of Reinforcement Learning in Uncertain Environments: A Simple Explanation for Complex Foraging Behaviors,” Adaptive Behavior, vol. 10, pp. 5-24, 2002.
Lilia Rejeb and Zahia Guessoum, “The Exploration-Exploitation Dilemma for Adaptive Agents,” in Proceedings of the 5th European Workshop on Adaptive Agents and Multi-Agent Systems (AAMASŠ05), March 2005.
Mikael Holmqvist,“Experiential Learning Processes of Exploitation and Exploration Within and Between Organizations: An Empirical Study of Product Development,” Organization Science, vol. 15, no. 1, pp. 70-81, 2004.
Matei Ripeanu, “Peer-to-Peer Architecture Case Study: Gnutella Network ,” in Proceedings of the 1st IEEE International Conference on Peer-to-Peer Computing (P2P2001), August 2001.
Elke Michlmayr, “Specification of the SemAnt Algorithm,” Tech. Rep., Vienna University of Technology, March 2006.
Marco Dorigo and Luca Maria Gambardella, “Ant Colony System: A Cooperative Learn-ing Approach to the Traveling Salesman Problem,” IEEE Transactions on Evolutionary Computation, vol. 1, pp. 53-66, 1997.
David E. Goldberg and Kalyanmoy Deb, “A Comparative Analysis of Selection Schemes Used in Genetic Algorithms,” in Proceedings of the 1st Workshop on Foundations of Genetic Algorithms, July 1990, pp. 69-93.
Jon M. Kleinberg, “Navigation in a small world,” Nature, vol. 406, pp. 845, August 2000.
Association for Computing Machinery, “ACM Computing Classification System (ACM CCS),” 1998.
Elke Michlmayr, Arno Pany, and Gerti Kappel, “Using Taxonomies for Content-based Routing with Ants,” in Proceedings of the Workshop on Innovations in Web Infrastruc-ture, 15th International World Wide Web Conference (WWW2006), May 2006.
Ricardo Baeza-Yates and Berthier Ribeiro-Neto, Modern Information Retrieval, Addison Wesley Longman Publishing Co. Inc., 1999.
Qin Lv, Pei Cao, Edith Cohen, and Scott Shenker, “Search and Replication in Unstruc-tured Peer-to-Peer Networks,” in Proceedings of the 16th ACM Conference on Super-computing, June 2002, pp. 84-95.
Ozalp Babaoglu, Hein Meling, and Alberto Montresor, “Anthill: A Framework for the Development of Agent-Based Peer-to-Peer Systems,” in Proceedings of the 22nd Inter-national Conference on Distributed Computing Systems (ICDCS 02). July 2002, IEEE.
Alberto Montresor, Hein Meling, and Ozalp Babaoglu, “Messor: Load-Balancing through a Swarm of Autonomous Agents,” in Proceedings of the 1st International Workshop on Agents and Peer-to-Peer Computing, July 2001.
Kurt Schelfthout and Tom Holvoet. A Pheromone-Based Coordination Mechanism Applied in Peer-to-Peer. In 2nd International Workshop on Agents and Peer-to-Peer Computing (AP2PC 2003), volume 2872 of Lecture Notes in Computer Science, pages 71-76. Springer, July 2003.
Ozalp Babaoglu, Geoffrey Canright, Andreas Deutsch, Gianni Di Caro, Frederick Ducatelle, Luca Maria Gambardella, Niloy Ganguly, Márk Jelasity, Roberto Monte-manni, and Alberto Montresor, “Design Patterns from Biology for Distributed Computing,” in Proceedings of the European Conference on Complex Systems (ECCS05), November 2005.
Arne Handt,“Self-Organizing Information Distribution in Peer-to-Peer Networks,” in Proceedings of New Trends in Network Architectures and Services: International Workshop on Self-Organizing Systems (IWSOS 2006), September 2006.
Jason Rohrer,“MUTE: Simple, Anonymous File Sharing,” http://mute-net.sourceforge.net/, 2005.
Jie Wu and Karl Aberer. Swarm Intelligent Surfing in the Web. In Proceedings of the 3rd International Conference on Web Engineering, July 2003.
T.H. Labella, M. Dorigo, and J.-L. Deneubourg. Division of labour in a group of robots inspired by ants’ foraging behavior. ACM Transactions on Autonomous and Adaptive Systems, 1(1):4-25, 2006.
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Michlmayr, E. (2007). Self-Organization for Search in Peer-to-Peer Networks. In: Dressler, F., Carreras, I. (eds) Advances in Biologically Inspired Information Systems. Studies in Computational Intelligence, vol 69. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72693-7_13
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