Journal is indexed in following databases:



2023 Journal Impact Factor - 0.7
2023 CiteScore - 1.4



HomePage
 




 


 

ISSN 2083-6473
ISSN 2083-6481 (electronic version)
 

 

 

Editor-in-Chief

Associate Editor
Prof. Tomasz Neumann
 

Published by
TransNav, Faculty of Navigation
Gdynia Maritime University
3, John Paul II Avenue
81-345 Gdynia, POLAND
www http://www.transnav.eu
e-mail transnav@umg.edu.pl
Multirole Population of Automated Helmsmen in Neuroevolutionary Ship Handling
1 Gdynia Maritime University, Gdynia, Poland
ABSTRACT: This paper presents the proposal of advanced intelligent system able to simulate and demonstrate 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. Individuals are being organized in groups specialized for particular task in ship maneuvering process. Neuroevolutionary algorithms, which develop artificial neural networks through evolutionary operations, are used in this system.
REFERENCES
Beyer, H.-G. & Schwefel, P. H. 2002. Evolution strategies – A comprehensive introduction. Natural Computing, 1(1):3–52.
Braun, H. & Weisbrod, J. 1993. Evolving feedforward neural networks. Proceedings of ANNGA93, International Conference on Artificial Neural Networks and Genetic Algorithms. Berlin: Springer.
Chu T. C., Lin Y. C. 2003. A Fuzzy TOPSIS Method for Robot Selection, the International Journal of Advanced Manufacturing Technology: 284-290,
Filipowicz, W., Łącki, M. & Szłapczyńska, J. 2005, Multicriteria decision support for vessels routing, Proceedings of ESREL’05 Conference.
Kaelbling, L. P., Littman & Moore. 1996. Reinforcement Learning: A Survey.
Kenneth, O.S., & Miikkulainen, R. 2002. Efficient Evolution of Neural Network Topologies, Proceedings of the 2002 Congress on Evolutionary Computation, Piscataway.
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.
Łącki, M. 2007 Machine Learning Algorithms in Decision Making Support in Ship Handling, Proceedings of TST Conference, Katowice-Ustroń, WKŁ.
Łącki, M. 2008, Neuroevolutionary approach towards ship handling, Proceedings of TST Conference, Katowice-Ustroń, WKŁ.
Łącki, M. 2009b, Ewolucyjne sieci NEAT w sterowaniu statkiem, Inżynieria Wiedzy i Systemy Ekspertowe, Akademicka Oficyna Wydawnicza EXIT, Warszawa, p. 535-544.
Łącki, M. 2010a, Wyznaczanie punktów trasy w neuroewolucyjnym sterowaniu statkiem. Logistyka, No 6.
Łącki, M. 2010b, Model środowiska wieloagentowego w neuroewolucyjnym sterowaniu statkiem. Zeszyty Naukowe Akademii Morskiej w Gdyni, No 67, p. 31-37.
Spears, W. 1995. Speciation using tag bits. Handbook of Evolutionary Computation. IOP Publishing Ltd. and Oxford University Press.
Stanley, K. O. & Miikkulainen, R. 2002. Efficient reinforcement learning through evolving neural network topologies. Proceedings of the Genetic and Evolutionary Computation. Conference (GECCO-2002). San 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.
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.
Sutton, R. & Barto, A. 1998. Reinforcement Learning: An Introduction.
Tesauro, G. 1995. Temporal Difference Learning and TD-Gammon, Communications of the Association for Computing Machinery, vol. 38, No. 3.
Citation note:
Łącki M.: Multirole Population of Automated Helmsmen in Neuroevolutionary Ship Handling. TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation, Vol. 5, No. 2, pp. 255-260, 2011

Other publications of authors:


File downloaded 817 times








Important: TransNav.eu cookie usage
The TransNav.eu website uses certain cookies. A cookie is a text-only string of information that the TransNav.EU website transfers to the cookie file of the browser on your computer. Cookies allow the TransNav.eu website to perform properly and remember your browsing history. Cookies also help a website to arrange content to match your preferred interests more quickly. Cookies alone cannot be used to identify you.
Akceptuję pliki cookies z tej strony