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Rough Ethology: Towards a Biologically-Inspired Study of Collective Behavior in Intelligent Systems with Approximation Spaces

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Transactions on Rough Sets III

Part of the book series: Lecture Notes in Computer Science ((TRS,volume 3400))

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Abstract

This article introduces an ethological approach to evaluating biologically-inspired collective behavior in intelligent systems. This is made possible by considering ethology (ways to explain agent behavior) in the context of approximation spaces. The aims and methods of ethology in the study of the behavior of biological organisms were introduced by Niko Tinbergen in 1963. The rough set approach introduced by Zdzisław Pawlak provides a ground for concluding to what degree a particular behavior for an intelligent system is a part of a set of behaviors representing a norm or standard. A rough set approach to ethology in studying the behavior of cooperating agents is introduced. Approximation spaces are used to derive action-based reference rewards for a swarm. Three different approaches to projecting rewards are considered as a part of a study of learning in real-time by a swarm. The contribution of this article is the introduction of an approach to rewarding swarm behavior in the context of an approximation space.

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References

  1. Alpigini, J.J., Peters, J.F., Skowron, A., Zhong, N. (eds.): RSCTC 2002. LNCS (LNAI), vol. 2475. Springer, Heidelberg (2002)

    MATH  Google Scholar 

  2. Applewhite, A.: The view from the top. IEEE Spectrum, 36–51 (November 2004)

    Google Scholar 

  3. Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm Intelligence. From Natural to Artificial Systems. Oxford University Press, UK (1999)

    MATH  Google Scholar 

  4. Cheng, K.: Generalization and Tinbergen’s four whys. Behavioral and Brain Sciences 24, 660–661 (2001)

    Article  Google Scholar 

  5. Dorigo, M.: Swarmbots. Wired, 119 (February 2004)

    Google Scholar 

  6. Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. Wiley-Interscience, Toronto (2001)

    Google Scholar 

  7. Fahle, M., Poggio, T. (eds.): Perceptual Learning. The MIT Press, Cambridge (2002)

    Google Scholar 

  8. Geppert, L.: Sony’s Orio. IEEE Spectrum (February 2004)

    Google Scholar 

  9. Harnad, S. (ed.): Categorical Perception. The Groundwork of cognition. Cambridge University Press, UK (1987)

    Google Scholar 

  10. Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning. Data Mining, Inference, and Prediction, Springer, Berlin (2001)

    Google Scholar 

  11. Holt, J.: UML for Systems Engineering. Watching the Wheels, The Institute of Electrical Engineers, Herts, UK (2001)

    Google Scholar 

  12. Kruuk, H.: Niko’s Nature. In: A life of Niko Tinbergen and his science of animal behavior, Oxford University Press, London (2003)

    Google Scholar 

  13. Lehner, P.N.: Handbook of Ethological Methods, 2nd edn. Cambridge University Press, UK (1996)

    Google Scholar 

  14. Martin, P., Bateson, P.: Measuring Behavior. Cambridge University Press, Cambridge (1993)

    Google Scholar 

  15. Mondada, F., Bonani, M., Magnenat, S., Guignard, A., Floreano, D.: Physical connections and cooperation in swarm robotics. In: Groen, F., Amato, N., Bonarini, A., Yoshida, E., Kröse, B. (eds.) Proceedings of the 8th Conference on Intelligent Autonomous Systems (IAS8), Amsterdam, NL, March 10-14, pp. 53–60 (2004)

    Google Scholar 

  16. Nguyen, S.H., Bazan, J., Skowron, A., Nguyen, H.S.: Layered learning for concept synthesis. In: Peters, J.F., Skowron, A., Grzymała-Busse, J.W., Kostek, B.z., Świniarski, R.W., Szczuka, M.S. (eds.) Transactions on Rough Sets I. LNCS, vol. 3100, pp. 187–208. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  17. Son, N.H., Skowron, A., Szczuka, M.S.: Analysis of image sequences for the Unmanned Aerial Vehicle. In: Hirano, S., Inuiguchi, M., Tsumoto, S. (eds.) Bulletin of the International Rough Set Society, vol.  5(1/2), pp. 185–184 (2001)

    Google Scholar 

  18. OMG Unified Modeling Language (UML) Specification. Object Management Group, http://www.omg.org

  19. Pal, S.K., Polkowski, L., Skowron, A. (eds.): Rough-Neural Computing. Techniques for Computing with Words. Springer, Heidelberg (2004)

    MATH  Google Scholar 

  20. Pawlak, Z.: Rough sets. International J. Comp. Inform. Science 11, 341–356 (1982)

    Article  MATH  MathSciNet  Google Scholar 

  21. Pawlak, Z.: Rough sets and decision tables. LNCS, vol. 208, pp. 186–196. Springer, Berlin (1985)

    Google Scholar 

  22. Pawlak, Z.: On rough dependency of attributes in information systems. Bulletin Polish Acad. Sci. Tech. 33, 551–599 (1985)

    MATH  MathSciNet  Google Scholar 

  23. Pawlak, Z.: On decision tables. Bulletin Polish Acad. Sci. Tech. 34, 553–572 (1986)

    Google Scholar 

  24. Pawlak, Z.: Decision tables—a rough set approach. Bulletin ETACS 33, 85–96 (1987)

    MATH  Google Scholar 

  25. Pawlak, Z.: Elementary rough set granules: Toward a rough set processor. In: [19], pp. 5–14 (2004)

    Google Scholar 

  26. Pawlak, Z.: Rough Sets. Theoretical Reasoning about Data. Kluwer, Dordrecht (1991)

    MATH  Google Scholar 

  27. Pawlak, Z., Skowron, A.: Rough membership functions. In: Yager, R., et al. (eds.) Advances in Dempster Shafer Theory of Evidence, pp. 251–271. Wiley, N.Y (1994)

    Google Scholar 

  28. Pawlak, Z.: Some issues on rough sets. Transactions on Rough Sets I, 1–58 (2004)

    Google Scholar 

  29. Pawlak, Z.: In pursuit of patterns in data reasoning from data–The rough set way. In: [1], pp. 1–9 (2002)

    Google Scholar 

  30. Pawlak, Z.: Rough sets and decision algorithms. In: [72], pp. 30–45 (2001)

    Google Scholar 

  31. Pawlak, Z.: Flow graphs and decision algorithms. In: [69], pp. 1–10 (2003)

    Google Scholar 

  32. Peters, J.F.: Design patterns in intelligent systems. In: Zhong, N., Raś, Z.W., Tsumoto, S., Suzuki, E. (eds.) ISMIS 2003. LNCS (LNAI), vol. 2871, pp. 262–269. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  33. Peters, J.F., Ramanna, S.: Intelligent systems design and architectural patterns. In: Proceedings IEEE Pacific Rim Conference on Communication, Computers and Signal Processing (PACRIM 2003), pp. 808–811 (2003)

    Google Scholar 

  34. Peters, J.F.: Approximation space for intelligent system design patterns. Engineering Applications of Artificial Intelligence 17(4), 1–8 (2004)

    Article  Google Scholar 

  35. Peters, J.F., Ramanna, S.: Measuring acceptance of intelligent system models. In: Negoita, M.G., Howlett, R.J., Jain, L.C. (eds.) KES 2004. LNCS (LNAI), vol. 3213, pp. 764–771. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  36. Peters, J.F.: Approximation spaces for hierarchical intelligent behavioral system models. In: Keplicz, B.D., Jankowski, A., Skowron, A., Szczuka, M. (eds.) Monitoring, Security and Rescue Techniques in Multiagent Systems. Advances in Soft Computing, pp. 13–30. Physica-Verlag, Heidelberg (2004)

    Google Scholar 

  37. Peters, J.F., Ramanna, S.: Approximation space for software models. Transactions on Rough Sets I, 338–355 (2004)

    Google Scholar 

  38. Peters, J.F., Skowron, A., Stepaniuk, J., Ramanna, S.: Towards an ontology of approximate reason. Fundamenta Informaticae 51(1,2), 157–173 (2002)

    MATH  MathSciNet  Google Scholar 

  39. Peters, J.F., Ahn, T.C., Borkowski, M., Degtyaryov, V., Ramanna, S.: Linecrawling robot navigation: A rough neurocomputing approach. In: Zhou, C., Maravall, D., Ruan, D. (eds.) Autonomous Robotic Systems. Studies in Fuzziness and Soft Computing, vol. 116, pp. 141–164. Physica-Verlag, Heidelberg (2003)

    Google Scholar 

  40. Peters, J.F., Ahn, T.C., Borkowski, M.: Object-classification by a line-crawling robot: A rough neurocomputing approach. In: Alpigini, J.J., Peters, J.F., Skowron, A., Zhong, N. (eds.) RSCTC 2002. LNCS (LNAI), vol. 2475, pp. 595–601. Springer, Heidelberg (2002)

    Google Scholar 

  41. Peters, J.F., Skowron, A., Synak, P., Ramanna, S.: Rough sets and information granulation. In: Bilgic, T., Baets, D., Kaynak, O. (eds.) IFSA 2003. LNCS, vol. 2715, pp. 370–377. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  42. Polkowski, L., Skowron, A. (eds.): Rough Sets in Knowledge Discovery 1. Studies in Fuzziness and Soft Computing, vol. 18. Physica-Verlag, Heidelberg (1998)

    MATH  Google Scholar 

  43. Polkowski, L., Skowron, A. (eds.): Rough Sets in Knowledge Discovery 2. Studies in Fuzziness and Soft Computing, vol. 19. Physica-Verlag, Heidelberg (1998)

    Google Scholar 

  44. Polkowski, L., Skowron, A.: Rough mereology: A new paradigm for approximate reasoning. Int. J. Approximate Reasoning 15/4, 333–365 (1996)

    Google Scholar 

  45. Polkowski, L., Skowron, A.: Rough meriological calculi of granules: A rough set approach to computation. Computational Intelligence 17(3), 472–492 (2001)

    Article  MathSciNet  Google Scholar 

  46. Polkowski, L.: Rough Sets. Mathematical Foundations. Physica–Verlag, Heidelberg (2002)

    MATH  Google Scholar 

  47. Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, Amsterdam (1988)

    Google Scholar 

  48. Skowron, A.: Toward intelligent systems: Calculi of information granules. In: Hirano, S., Inuiguchi, M., Tsumoto, S. (eds.) Bulletin of the International Rough Set Society, vol. 5(1/2), pp. 9–30 (2001)

    Google Scholar 

  49. Skowron, A., Peters, J.F.: Rough sets: Trends and Challenges. In: [69], pp. 25–34 (2003)

    Google Scholar 

  50. Skowron, A., Stepaniuk, J.: Information systems in hierarchical modeling. In: Lindemann, G., Burkhard, H.-D., Czaja, L. (eds.) Proceedings of the Workshop on Concurrency, Specification and Programming (CSP 2004), Caputh, Germany, September 24-26, Informatik- Bericht Nr. 170, vol. 1-3, pp. 378–389. Humboldt Universität (2004)

    Google Scholar 

  51. Skowron, A., Synak, P., Peters, J.F.: Spacio-temporal approximate reasoning over hierarchical information maps. In: Lindemann, G., Burkhard, H.-D., Czaja, L., Skowron, A., Schlingloff, H., Suraj, Z. (eds.) Proceedings of the Workshop on Concurrency, Specification and Programming (CSP 2004), Caputh, Germany, September 24–26, Informatik-Bericht Nr. 170, pp. 358–371. Humboldt Universität (2004)

    Google Scholar 

  52. Skowron, A., Stepaniuk, J.: Generalized approximation spaces. In: Proceedings of the Third International Workshop on Rough Sets and Soft Computing, San Jose, pp. 156–163 (1994)

    Google Scholar 

  53. Skowron, A., Stepaniuk, J.: Generalized approximation spaces. In: Lin, T.Y., Wildberger, A.M. (eds.) Soft Computing, Simulation Councils, San Diego, pp. 18–21 (1995)

    Google Scholar 

  54. Skowron, A., Stepaniuk, J.: Tolerance approximation spaces. Fundamenta Informaticae 27, 245–253 (1996)

    MATH  MathSciNet  Google Scholar 

  55. Skowron, A., Stepaniuk, J.: Information granules and approximation spaces. In: Proc. of the 7th Int. Conf. on Information Processing and Management of Uncertainty in Knowledge-based Systems (IPMU 1998), Paris, pp. 1354–1361 (1998)

    Google Scholar 

  56. Skowron, A., Stepaniuk, J.: Information granules and rough neural computing. In: [19], pp. 43–84 (2004)

    Google Scholar 

  57. Skowron, A., Stepaniuk, J.: Information granules: Towards Foundations of Granular Computing. Int. Journal of Intelligent Systems 16, 57–85 (2001)

    Article  MATH  Google Scholar 

  58. Skowron, A., Stepaniuk, J., Peters, J.F.: Rough sets and infomorphisms: Towards approximation of relations in distributed environments. Fundamenta Informaticae 54(2,3), 263–277 (2003)

    MATH  MathSciNet  Google Scholar 

  59. Skowron, A., Swiniarski, R.W.: Information granulation and pattern recognition. In: [19], pp. 599–636 (2004)

    Google Scholar 

  60. Skowron, A., Swiniarski, R., Synak, P.: Approximation spaces and information granulation. In: Tsumoto, S., Słowiński, R., Komorowski, J., Grzymała-Busse, J.W. (eds.) RSCTC 2004. LNCS (LNAI), vol. 3066, pp. 116–126. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  61. Skowron, A., Stepaniuk, J.: Information granules in distributed environment. In: Zhong, N., Skowron, A., Ohsuga, S. (eds.) RSFDGrC 1999. LNCS (LNAI), vol. 1711, pp. 357–366. Springer, Heidelberg (1999)

    Chapter  Google Scholar 

  62. Skowron, A., Stepaniuk, J.: Towards discovery of information granules. In: Żytkow, J.M., Rauch, J. (eds.) PKDD 1999. LNCS (LNAI), vol. 1704, pp. 542–547. Springer, Heidelberg (1999)

    Chapter  Google Scholar 

  63. Skowron, A., Stepaniuk, J.: Constraint sums in information systems. In: Tsumoto, S., Słowiński, R., Komorowski, J., Grzymała-Busse, J.W. (eds.) RSCTC 2004. LNCS (LNAI), vol. 3066, pp. 300–309. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  64. Stepaniuk, J.: Approximation spaces, reducts and representatives. In: [43], pp. 109–126

    Google Scholar 

  65. Stone, P.: Layered Learning in Multiagent Systems. A Winning Approach to Robotic Soccer, The MIT Press, Cambridge (2000)

    Google Scholar 

  66. Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. The MIT Press, Cambridge (1998)

    Google Scholar 

  67. Tinbergen, N.: On aims and methods of ethology. Zeitschrift für Tierpsychologie 20, 410–433 (1963)

    Article  Google Scholar 

  68. Tinbergen, N.: Social Behavior in Animals with Special Reference to Vertebrates. The Scientific Book Club, London (1953)

    Google Scholar 

  69. Wang, G., Liu, Q., Yao, Y., Skowron, A. (eds.): RSFDGrC 2003. LNCS (LNAI), vol. 2639. Springer, Heidelberg (2003)

    MATH  Google Scholar 

  70. Watanabe, S.: Pattern Recognition: Human and Mechanical. Wiley, Toronto (1985)

    Google Scholar 

  71. WITAS project (2001), http://www.ida.liu.se/ext/witas/eng.html

  72. Ziarko, W., Yao, Y. (eds.): RSCTC 2000. LNCS (LNAI), vol. 2005. Springer, Heidelberg (2001)

    Google Scholar 

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Manitoba, Winnipeg, Manitoba, R3T 5V6, Canada

    James F. Peters

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  1. James F. Peters
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  1. Department of Electrical and Computer Engineering, University of Manitoba, R3T 5V6, Winnipeg, Manitoba, Canada

    James F. Peters

  2. Institute of Mathematics, Warsaw University, Banacha 2, 02-097, Warsaw, Poland

    Andrzej Skowron

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Peters, J.F. (2005). Rough Ethology: Towards a Biologically-Inspired Study of Collective Behavior in Intelligent Systems with Approximation Spaces. In: Peters, J.F., Skowron, A. (eds) Transactions on Rough Sets III. Lecture Notes in Computer Science, vol 3400. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427834_7

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  • DOI: https://doi.org/10.1007/11427834_7

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