R. A. Watson, S. G. Ficici, and J. B. Pollack. Embodied evolution: Embodying an evolutionary algorithm in a population of robots. In P. J. Angeline, Z. Michalewicz, M. Schoenauer, X. Yao, and A. Zalzala, editors, Proceedings of the Congress on Evolutionary Computation, volume 1, pages 335--342, Mayflower Hotel, Washington D.C., USA, 6-9 1999. IEEE Press.  Home/Search Document Details and Download
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.... the remainder of the topics, which introduce the general areas in robotics: effectors, sensors and control [23, 18] The area of control is covered in more depth, discussing various architectures including deliberative, reactive, hybrid and behaviorbased [8, 1, 6, 22] Learning is also discussed [21, 13, 35]. Other topics presented include artificial life [10, 3] edutainment [14, 32] cognitive science and psychology [24, 7] and science fiction [2] The course is taught over a 14 week semester. There is one 75 minute lecture and one 75 minute lab per week. There are two exams, and students submit ....
R. A. Watson, S. G. Ficici, and J. B. Pollack. Embodied Evolution: Embodying an Evolutionary Algorithm in a Population of Robots. In Proc of CEC-99, 1999.
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....contributions described in this paper are the development of a graph grammar based representation scheme for Lego Assemblies and its encoding as an assembly graph for manipulation and evolution by Genetic Algorithms. This graph based approach is unlike other systems for Lego design evolution [7], and we believe that this assembly graphbased representation scheme is one of the most general ways of representing the assembly, and in this way provides a more flexible means to represent a wide variety of mechanisms for use with GAs. Acknowledgments Support provided by the NSF Knowledge and ....
J. Pollack, R. Watson, S. Ficici (1999). Embodied Evolution: Embodying an Evolutionary Algorithm in a Population of Robots. 1999 Congress on Evolutionary Computation. Angeline, Michalewicz, Schoenauer, Yao, Zalzala, eds. IEEE Press, 335-342.
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....for successful individuals to breed. Thus each individual agent would represent a good search strategy for the neighbourhood in which 12 it is located, and ecological notions such as crowding could be used to control the exploration of the search space. Similar ideas have been used in robotics [98] and database searching [27, 69, 70] This approach may be more scalable than the GA approach, as the population can split into smaller subgroups whereas in the GA approach the population will often follow a single niche until it is fully exploited, then move onto another niche, rather than ....
R. A. Watson, S. G. Ficici, and J. B. Pollack. Embodied evolution: Embodying an evolutionary algorithm in a population of robots. In P. Angeline, Z. Michalewicz, M. Schoenhauer, X. Yao, and A. Zalzala, editors, Proceedings of the 1999 Congress on Evolutionary Computation, pages 335-342. IEEE Press, 1999.
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....We tried to use very simple approaches to solve the problem in order to nd the lower bounds of robot and algorithm complexity needed to eat all the food pellets lying along the trail. This task turned out to be a real challenge, particularly for the real world setup. Embodied evolution (EE) [7,18] avoids the pitfalls of the simulate and transfer approach. Ficici et al. describe the method as follows: iWe dene EE as evolution taking place within a population of real robots where evaluation, selection, and reproduction are carried out by and between the robots in a distributed, asynchronous, ....
R. A Watson, S. G. Ficici, and J. B. Pollack. Embodied Evolution: Embodying an Evolutionary Algorithm in a Population of Robots. In Congress on Evolutionary Computation CEC'99, volume 1, pages 353342, Washington D.C., USA, July 6-9 1999. IEEE.
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R. A. Watson, S. G. Ficici, and J. B. Pollack. Embodied evolution: Embodying an evolutionary algorithm in a population of robots. In P. J. Angeline, Z. Michalewicz, M. Schoenauer, X. Yao, and A. Zalzala, editors, Proceedings of the Congress on Evolutionary Computation, volume 1, pages 335--342, Mayflower Hotel, Washington D.C., USA, 6-9 1999. IEEE Press.
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R. Watson, S. Ficici, and J. B. Pollack. Embodied evolution: Embodying an evolutionary algorithm in a population of robots. In P. Angeline, Z. Michalewicz, M. Schoenauer, X. Yao, and A. Zalzala, editors, 1999.
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R. A. Watson, S. G. Ficici, and J. B. Pollack. Embodied evolution: Embodying an evolutionary algorithm in a population of robots. In P. J. Angeline, Z. Michalewicz, M. Schoenauer, X. Yao, and A. Zalzala, editors, Proceedings of the Congress on Evolutionary Computation, volume 1, pages 335--342, Mayflower Hotel, Washington D.C., USA, 6-9 1999. IEEE Press.
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R. A. Watson, S. G. Ficici, and J. B. Pollack. Embodied evolution: Embodying an evolutionary algorithm in a population of robots. In P. Angeline, Z. Michalewicz, M. Schoenauer, X. Yao, and A. Zalzala, editors, 1999.
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R. Watson, S. Ficici, and J. B. Pollack. Embodied evolution: Embodying an evolutionary algorithm in a population of robots. In P. Angeline, Z. Michalewicz, M. Schoenauer, X. Yao, and A. Zalzala, editors, 1999.
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R. Watson, S. Ficici, and J. B. Pollack. Embodied evolution: Embodying an evolutionary algorithm in a population of robots. In P. Angeline, Z. Michalewicz, M. Schoenauer, X. Yao, and A. Zalzala, editors, 1999.
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....[30] and design of complex algorithms like sorting networks [18] and cellular automata [20] In our next generations of evolved creatures we expect to see some sensor integration, and we have already demonstrated robot cultural evolution, learning from interacting in the real environment. [35] The issue of whether or not this kind of artificial life work will ever be practical and scaleable is best related to the history of computer chess. The theory that machines could play a game like chess was from the 1920 s. The first chess playing computer was built in the mid 1950 s, and made ....
R. Watson, S. Ficici, and J. B. Pollack. Embodied evolution: Embodying an evolutionary algorithm in a population of robots. In P. Angeline, Z. Michalewicz, M. Schoenauer, X. Yao, and A. Zalzala, editors, 1999 Congress on Evolutionary Computation, 1999. 18
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....evolutionary method allows the potential for being scaled to very large populations of robots, on the order of hundreds or thousands, thus enabling speedup that is critical when using evolution in real robots. Technologically, this introduces two main problems: long term power and reprogramming [28,29]. Many robots batteries last only for a few hours, and robots typically have to be attached to a PC for new programs to be uploaded. In order to do large group robot learning experiments, we have designed a continuouspower oor system, and utilized infra red (IR) communications to transfer ....
Watson, R., Ficici, S., Pollack, J.: Embodied evolution: Embodying an evolutionary algorithm in a population of robots. In Angeline, P., Michalewicz, Z., Schoenauer, M., Yao, X., Zalzala, A., eds.: 1999 Congress on Evolutionary Computation. (1999)
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....each send, the rate of broadcasting decays exponentially over the time from its most recent visit to the light. The energy level thus approximates a leaky integral of the robot s performance at its task (i.e. the frequency with which it reaches the light) Experimental details can be found in (Watson et al. 1999). 3.2 Results Figure 1 shows the frequency with which the light is successfully reached by the robot population over time in each of three experiments. The main experiment evolves the neural network weights to perform the light seeking task. The initial condition for the networks is that all ....
Watson, R. A., Ficici, S. G., and Pollack, J. B. (1999). Embodied evolution: Embodying an evolutionary algorithm in a population of robots. In Angeline, P., Michalewicz, Z., Schoenauer, M., Yao, X., and Zalzala, A., editors, 1999 Congress on Evolutionary Computation, pages 335--342. IEEE Press.
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R. A. Watson, S. G. Ficici and J. B. Pollack, Embodied evolution: Embodying an evolutionary algorithm in a population of robots, Proc. Congr. Evol. Comput. (1999) 335--342.
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J. Pollack, R. Watson, S. Ficici (1999). Embodied Evolution: Embodying an Evolutionary Algorithm in a Population of Robots. 1999 Congress on Evolutionary Computation. Angeline, Michalewicz, Schoenauer, Yao, Zalzala, eds. IEEE Press, 335-342.
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