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Computer study of the evolution of ‘news foragers' on the Internet

  • Zsolt Palotai
  • Sándor Mandusitz
  • András Lórincz
Part of the Studies in Computational Intelligence book series (SCI, volume 34)

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References

  1. 1.
    R. Albert and A.L. Barab ási. Statistical mechanics of complex networks. Reviews of Modern Physics, 74:47-91, 2002.CrossRefMathSciNetGoogle Scholar
  2. 2.
    . N. Angkawattanawit and A. Rungsawang. Learnable topic-specific web crawler. In A. Abraham, J. Ruiz-del-Solar, and M. K öppen, editors, Hybrid Intelligent Systems, pages 573-582. IOS Press, 2002.Google Scholar
  3. 3.
    A.L. Barab ási, R. Albert, and H. Jeong. Scale-free characteristics of random networks: The topology of the world wide web. Physica A, 281:69-77, 2000.CrossRefGoogle Scholar
  4. 4.
    M. Bedau, J. McCaskill, N. Packard, S. Rasmussen, C. Adami, D. Green, T. Ikegami, K. Kaneko, and T. Ray. Open problems in artificial life. Artificial Life, 6:363-376, 2000.CrossRefGoogle Scholar
  5. 5.
    D.L. Boley. Principal direction division partitioning. Data Mining and Knowledge Discovery, 2:325-244, 1998.CrossRefGoogle Scholar
  6. 6.
    J. Cho and H. Garcia-Molina. Effective page refresh policies for web crawlers. ACM Transactions on Database Systems, 28(4):390-426, 2003.CrossRefGoogle Scholar
  7. 7.
    C.W. Clark and M. Mangel. Dynamic State Variable Models in Ecology: Methods and Applications. Oxford University Press, Oxford UK, 2000.Google Scholar
  8. 8.
    P. Crucitti, V. Latora, M. Marchiori, and A. Rapisarda. Efficiency of scale-free networks: Error and attack tolerance. Physica A, 320:622-642, 2003.MATHCrossRefGoogle Scholar
  9. 9.
    V. Cs ányi. Evolutionary Systems and Society: A General Theory of Life, Mind, and Culture. Duke University Press, Durham, NC, 1989.Google Scholar
  10. 10.
    . J. Edwards, K. McCurley, and J. Tomlin. An adaptive model for optimizing performance of an incremental web crawler. In Proceedings of the tenth international conference on World Wide Web, pages 106-113, 2001.Google Scholar
  11. 11.
    J.M. Fryxell and P. Lundberg. Individual Behavior and Community Dynamics. Chapman and Hall, London, 1998.Google Scholar
  12. 12.
    . T. Joachims. A probabilistic analysis of the Rocchio algorithm with TFIDF for text categorization. In Douglas H. Fisher, editor, Proceedings of ICML-97, 14 th International Conference on Machine Learning, pages 143-151, Nashville, US, 1997. Morgan Kaufmann Publishers, San Francisco, US.Google Scholar
  13. 13.
    G. Kampis. Self-modifying Systems in Biology and Cognitive Science: A New Framework for Dynamics, Information and Complexity. Pergamon, Oxford UK, 1991.Google Scholar
  14. 14.
    J. Kennedy, R.C. Eberhart, and Y. Shi. Swarm Intelligence. Morgan Kaufmann, San Francisco, USA, 2001.Google Scholar
  15. 15.
    J. Kleinberg and S. Lawrence. The structure of the web. Science, 294:1849-1850, 2001.CrossRefGoogle Scholar
  16. 16.
    I. K ókai and A. L őrincz. Fast adapting value estimation based hybrid architecture for searching the world-wide web. Applied Soft Computing, 2:11-23, 2002.CrossRefGoogle Scholar
  17. 17.
    . R. Lempel and S. Moran. The stochastic approach for link-structure analysis (salsa) and the tkc effect. Computer Networks, 33, 2000.Google Scholar
  18. 18.
    A. L őrincz, I. K ókai, and A. Meretei. Intelligent high-performance crawlers used to reveal topic-specific structure of the WWW. Int. J. Founds. Comp. Sci., 13:477-495, 2002.CrossRefGoogle Scholar
  19. 19.
    M.J. Mataric. Reinforcement learning in the multi-robot domain. Autonomous Robots, 4(1):73-83, 1997.CrossRefGoogle Scholar
  20. 20.
    F. Menczer. Complementing search engines with online web mining agents. Decision Support Systems, 35:195-212, 2003.CrossRefGoogle Scholar
  21. 21.
    E. Pachepsky, T. Taylor, and S. Jones. Mutualism promotes diversity and stability in a simple artificial ecosystem. Artificial Life, 8(1):5-24, 2002.CrossRefGoogle Scholar
  22. 22.
    Zs. Palotai, B. G ábor, and A. L őrincz. Adaptive highlighting of links to assist surfing on the internet. Int. J. of Information Technology and Decision Making, 4:117-139, 2005.CrossRefGoogle Scholar
  23. 23.
    S. Rasmussen, N.A. Baas, B. Mayer, M. Nilsson, and M.W. Olesen. Ansatz for dynamical hierarchies. Artificial Life, 7(4):329-354, 2001.CrossRefGoogle Scholar
  24. 24.
    K. M. Risvik and R. Michelsen. Search engines and web dynamics. Computer Networks, 32:289-302, 2002.CrossRefGoogle Scholar
  25. 25.
    W. Schultz. Multiple reward systems in the brain. Nature Review of Neuroscience, 1:199-207,2000.CrossRefGoogle Scholar
  26. 26.
    R. Sutton. Learning to predict by the method of temporal differences. Machine Learning, 3:9-44, 1988.Google Scholar
  27. 27.
    R. Sutton and A.G. Barto. Reinforcement Learning: An Introduction. MIT Press, Cambridge, 1998.Google Scholar
  28. 28.
    . I. Szita and A. L őrincz. Kalman filter control embedded into the reinforcement learning framework. Neural Computation, 2003. (in press).Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Zsolt Palotai
    • 1
  • Sándor Mandusitz
    • 2
  • András Lórincz
    • 3
  1. 1.Department of Information SystemsEötvös Loránd UniversityHungary
  2. 2.Department of Information SystemsEötvös Loránd UniversityHungary
  3. 3.Department of Information SystemsEötvös Loránd UniversityHungary

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