Using Assembler Encoding to build neuro-controllers for a team of autonomous underwater vehicles

• Autorzy: Praczyk T. , Szymak P.
• Języki publikacji: EN

Abstrakty

EN

The paper compares a neuro-evolutionary metod called Assembler Encoding with two other methods from the area of neuro–evolution. As a testbed for the methods a variant of the predator–prey problem with Autonomous Underwater Vehicles (AUV) operating in an environment with the sea current was used. In the experiments, the task of vehicles–predators controlled with evolutionary neural networks was to capture a vehicle–prey behaving according to a simple deterministic strategy. All the experiments were carried out in simulation, and in order to simplify calculations in the two–dimensional environment – AUVs moved on a horizontal surface under the water.

Słowa kluczowe

EN

evolutionary neural networks; autonomous underwater vehicles

• Wydawca: Systems Research Institute, Polish Academy of Sciences
• Czasopismo: Control and Cybernetics
• Rocznik: 2013
• Tom: Vol. 42, no. 1
• Strony: 267--286
• Opis fizyczny: Bibliogr. 26 poz., il.

Twórcy

autor

Praczyk T.

• Polish Naval Academy, Institute of Naval Weapon, Gdynia, Poland

autor

Szymak P.

• Polish Naval Academy, Institute of Electrical Engineering and Automatics Gdynia, Poland

Bibliografia

• 1. Cangelosi, A., Parisi, D. and Nolfi, S. (1994)Cell division and migration in a genotype for neural networks. Network: computation in neural systems 5(4), 497-515.
• 2. Elman, J. L. (1993) Learning and development in neural networks: The importance of starting small. Cognition, 48, 71-99.
• 3. Fossen, T.J. (1994)Guidance and Control of Ocean Vehicles. John Wiley and Sons Ltd.
• 4. Goldberg, D.E. (1989) Genetic Algorithms in Search, Optimization and Machine Learning. Addison Wesley, Reading, Massachusetts.
• 5. Gruau, F. (1994)Neural Network Synthesis Using Cellular Encoding And The Genetic Algorithm. PhD Thesis, Ecole Normale Superieure de Lyon.
• 6. Kitano, H. (1990) Designing neural networks using genetic algorithms with graph generation system. Complex Systems 4, 461-476.
• 7. Krawiec, K. and Bhanu, B. (2005) Visual Learning by Coevolutionary Feature Synthesis. IEEE Trans. on Systems, Man, and Cybernetics, Part B: Cybernetics. 35:409-425.
• 8. Kubaty, T. and Rowiski, L. (no date) Mine counter vehicles for Baltic Navy. Internet, http://www.underwater.pg.gda.pl.
• 9. Lang, R.I.W. (2000)A Future for Dynamic Neural Networks. University of Reading, CYB/1/PG/RIWL/V1.0.
• 10. Luke, S. and Spector, L. (1996) Evolving Graphs and Networks with Edge Encoding: Preliminary Repor. In: John R. Koza, ed., Late Breaking Papers at the Genetic Programming 1996 Conference., Stanford University, CA, USA. Stanford Bookstore, 117-124.
• 11. Miller, G.F., Todd, P.M. and Hegde S.U. (1989) Designing Neural Networks Using Genetic Algorithms. In: J.D. Schaffer, ed., Proc. of the Third International Conference on Genetic Algorithms. Morgan Kaufmann, San Mateo, 379-384.
• 12. Moriarty, D. E. (1997) Symbiotic Evolution of Neural Networks in Sequential Decision Tasks. PhD thesis, The University of Texas at Austin, TR UT-AI97-257.
• 13. Nolfi, S. and Parisi, D. (1992)Growing neural networks. In: C.G. Langton, ed., Artificial Life III. Addison-Wesley.
• 14. Nordin, P., Banzhaf, W. and Francone, F. (1999) Efficient Evolution of Machine Code for CISC Architectures using Blocks and Homologous Crossover. In: L. Spector et al., eds., Advances in Genetic Programming III. MIT Press, 275-299.
• 15. Potter, M. (1997) The Design and Analysis of a Computational Model of Cooperative Coevolution. PhD thesis, George Mason University, Fairfax, Virginia.
• 16. Potter, M. A. and De Jong, K. A. (2000)Cooperative coevolution: An architecture for evolving coadapted subcomponents. Evolutionary Computation 8(1), 1-29.
• 17. Praczyk, T. (2007) Evolving co-adapted subcomponents in Assembler Encoding. International Journal of Applied Mathematics and Computer Science 17(4)
• 18. Praczyk, T. (2008) Modular networks in Assembler Encoding. Computational Methods in Science and Technology, CMST 14(1), 27-38.
• 19. Praczyk, T. (2009)Concepts of learning in Assembler Encoding. Archives of Control Science, 18(3), 323-337 (2008)
• 20. Praczyk, T. (2009) Using assembler encoding to solve inverted pendulum problem. Computing and Informatics 28, 895-912.
• 21. Praczyk, T. (2010) Searching for optimal size neural networks in assembler encoding. Control and Cybernetics 39(4), 1193-1215.
• 22. Praczyk, T. (2011) Forming Neural Networks by Means of Assembler Encoding. Intelligent Automation and Soft Computing 17(3), 319-331.
• 23. Praczyk, T. and Szymak, P. (2011) Decision System for a Team of Autonomous Underwater Vehicles - Preliminary Report. Neurocomputing 74(17), 3323-3334.
• 24. Stanley, O. (2004)Efficient Evolution of Neural Networks Through Complexification. PhD thesis, Department of Computer Science, The University of Texas at Austin, Technical Report AI-TR-04-314.
• 25. Szymak, P. (2006) Using of fuzzy logic method to control of underwater vehicle in inspection of oceanotechnical objects. Polish Neural Network Society, Artificial Intelligence and Soft Computing. Academic Publishing House EXIT, 163-168.
• 26. Szymak, P. (2010) Simplified mathematical model of underwater vehicle and its control system (in Polish). Pomiary, Automatyka i Robotyka, 2/2010, Industrial Research Institute for Automation and Measurements, 372-379.