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Języki publikacji
Abstrakty
In this paper the author compares the efficiency of two encoding schemes for artificial intelligence methods used in the neuroevolutionary ship maneuvering system. This may be also be seen as the ship handling system that simulates a learning process of a group of artificial helmsmen - autonomous control units, created with an artificial neural network. The helmsman observes input signals derived form an enfironment and calculates the values of required parameters of the vessel maneuvering in confined waters. In neuroevolution such units are treated as individuals in population of artificial neural networks, which through environmental sensing and evolutionary algorithms learn to perform given task efficiently. The main task of this project is to evolve a population of helmsmen with indirect encoding and compare results of simulation with direct encoding method.
Rocznik
Tom
Strony
71--76
Opis fizyczny
Bibliogr. 13 poz., rys., tab.
Twórcy
autor
- Gdynia Maritime University, Gdynia, Poland
Bibliografia
- 1 Gruau, F. 1994. Neural Network Synthesis Using Cellular Encoding And The Genetic Algorithm.
- 2 Haasdijk, E., Rusu, A.A. & Eiben, A.E. 2010. HyperNEAT for Locomotion Control in Modular Robots.
- 3 Isherwood, J.W. 1973. Trans. R. Inst. Nav. Archit., Wind resistance of merchant ships, vol. 115, 327–332.
- 4 Kwaśnicka, H. 2007. Ewolucyjne projektowanie sieci neuronowych , Oficyna Wydawnicza Politechniki Wrocławskiej.
- 5 Łącki, M. 2007. Machine Learning Algorithms in Decision Making Support in Ship Handling. , Katowice‐Ustroń: WKŁ,
- 6 Łącki, M. 2012. TransNav ‐ Int. J. Mar. Navig. Saf. Sea Transp., Neuroevolutionary Ship Handling System in a Windy Environment, Vol. 6, No. 4, pp. 453‐458
- 7 Larkin, D., Kinane, A. & O’Connor, N. 2006. Towards hardware acceleration of neuroevolution for multimedia processing applications on mobile devices , Hong Kong, China.
- 8 Lee, S., Yosinski, J., Glette, K., Lipson, H. & Clune J 2013. Appl. Evol. Comput., Evolving gaits for physical robots with the HyperNEAT generative encoding: the benefits of simulation,.
- 9 Lehman, J. & Miikkulainen, R. 2013. Scholarpedia, Neuroevolution, vol. 8, 30977.
- 10 Nowak, A., Praczyk, T. & Szymak, P. 2008. Zeszty Nauk. Akad. Mar. Wojennej, Multi‐agent system of autonomous underwater vehicles ‐ preliminary report, vol. 4, 99–108.
- 11 OCIMF 1977. Prediction of Wind and Current Loads on VLCCs , Oil Companies International Marine Forum.
- 12 Stanley, K.O. & Risto, M. 2002. Efficient Reinforcement Learning Through Evolving Neural Network Topologies.
- 13 Stanley, K.O., Bryant, B.D. & Risto, M. 2005. IEEE Trans. Evol. Comput., Real‐time neuroevolution in the NERO video game, vol. 9, 653–668.
Uwagi
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-c7357acd-7021-4921-a27d-3125dbac684f