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Multirole Population of Automated Helmsmen in Neuroevolutionary Ship Handling

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EN
Abstrakty
EN
This paper presents the proposal of advanced intelligent system able to simulate and demon-strate learning behavior of helmsmen in ship maneuvering. Simulated helmsmen are treated as individuals in population, which through environmental sensing learn themselves to safely navigate on restricted waters. In-dividuals are being organized in groups specialized for particular task in ship maneuvering process. Neuroev-olutionary algorithms, which develop artificial neural networks through evolutionary operations, are used in this system.
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autor
  • Gdynia Maritime University, Gdynia, Poland
Bibliografia
  • [1] Beyer, H.-G. & Schwefel, P. H. 2002. Evolution strategies – A comprehensive introduction. Natural Computing, 1(1):3–52.
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  • Chu T. C., Lin Y. C. 2003. A Fuzzy TOPSIS Method for Robot Selection, the International Journal of Advanced [3] Manufacturing Technology: 284-290,
  • [4] Filipowicz, W., Łącki, M. & Szłapczyńska, J. 2005, Multicriteria decision support for vessels routing, Proceedings of ESREL’05 Conference.
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  • [7] Kenneth, O.S. & Miikkulainen R. 2005. Real-Time Neuroevolution in the NERO Video Game, Proceedings of the IEEE 2005 Symposium on Computational Intelligence and Games, Piscataway.
  • [8] Łącki, M. 2007 Machine Learning Algorithms in Decision Making Support in Ship Handling, Proceedings of TST Conference, Katowice-Ustroń, WKŁ.
  • [9] Łącki, M. 2008, Neuroevolutionary approach towards ship handling, Proceedings of TST Conference, Katowice-Ustroń, WKŁ.
  • [10] Łącki, M. 2009a, Speciation of population in neuroevolution-ary ship handling, Marine Navigation and Safety of Sea Transportation, CRC Press/Balkema, Taylor & Francis Group, Boca Raton – London - New York - Leiden, p. 541-545.
  • [11] Łącki, M. 2009b, Ewolucyjne sieci NEAT w sterowaniu statkiem, Inżynieria Wiedzy i Systemy Ekspertowe, Akade-micka Oficyna Wydawnicza EXIT, Warszawa, p. 535-544.
  • [12] Łącki, M. 2010a, Wyznaczanie punktów trasy w neuroewolucyjnym sterowaniu statkiem. Logistyka, No 6.
  • [14] Łącki, M. 2010b, Model środowiska wieloagentowego w neroewolucyjnym sterowaniu statkiem. Zeszyty Naukowe Akademii Morskiej w Gdyni, No 67, p. 31-37.
  • [14] Spears, W. 1995. Speciation using tag bits. Handbook of Evolutionary Computation. IOP Publishing Ltd. and Oxford University Press.
  • [15] Stanley, K. O. & Miikkulainen, R. 2002. Efficient reinforce-ment learning through evolving neural network topologies. Proceedings of the Genetic and Evolutionary Computation. Conference (GECCO-2002). San [16] Francisco, CA: Morgan Kaufmann.
  • Stanley, K. O. & Miikkulainen, R. 2005. Real-Time Neuroevolution in the NERO Video Game, Proceedings of the IEEE 2005 Symposium on Computational Intelligence and Games, Piscataway.
  • [17] Sutton, R. 1996. Generalization in Reinforcement Learning: Successful Examples Using Sparse Coarse Coding. Touretzky, D., Mozer, M., & Hasselmo, M. (Eds.), Neural Information Processing Systems 8.
  • [18] Sutton, R. & Barto, A. 1998. Reinforcement Learning: An Introduction.
  • [19] Tesauro, G. 1995. Temporal Difference Learning and TD-Gammon, Communications of the Association for Computing Machinery, vol. 38, No. 3.
Typ dokumentu
Bibliografia
Identyfikator YADDA
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