H. Juill and J. B. Pollack. Dynamics of co-evolutionary learning. In Proceedings of the Fourth International Conference on Simulation of Adaptive Behavior, pages 526--534. MIT Press, 1996.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....what they know, to what they almost know. What is missing We believe that a model of self motivation could drive the system to continued development. Such internally driven artificial systems are rare. One such mechanism is the competitive arms race of co evolutionary systems (see, for example, (Juille Pollack 1996)) The basic idea of the competitive arms race is that two populations are pitted against one another, and gradually one up each other in a spiralling increase of fitness. This works if the two populations begin about equal, and remain relatively even throughout the race. Our goal is to find a ....

Juille, H., and Pollack, J. B. 1996. Dynamics of co-evolutionary learning. In Proceedings of the Fourth International Conference on Simulation of Adaptive Behavior, 526--534. MIT Press.

....exact nature of such a mechanism has yet to be explored. We believe that a model of self motivation could drive the system to continued development. Such internally driven arti cial systems are rare. One such mechanism is the competitive arms race of co evolutionary systems (see, for example, [3]) The basic idea of the competitive arms race is that two populations are pitted against one another, and gradually one up each other in a spiraling increase of tness. This works if the two populations begin about equal, and remain relatively even throughout the race. 4 Toward Purposeful ....

H. Juille and J. B. Pollack. Dynamics of co-evolutionary learning. In Proceedings of the Fourth International Conference on Simulation of Adaptive Behavior, pages 526-534. MIT Press, 1996.

.... solved by the rst, but not by the second, is greater than the number solved by the second, but not the rst [6] and resource sharing tness functions, in which strategies receive a higher tness if they are able to solve test cases that are unsolvable by a large fraction of other strategies [5, 6, 10, 11]. Department of Physics, Princeton University, Princeton, NJ 08544 (email: jkwerfel princeton.edu) y Santa Fe Institute, 1399 Hyde Park Road, Santa Fe, NM 87501 (email: fmm, chaosg santafe.edu) The motivation behind resource sharing is to promote diversity by rewarding strategies that can ....

....test cases enter temporal oscillations in which each in turn performs well against the other population. The individuals in both populations, however, generally perform poorly against opponents chosen from outside the populations. Resource sharing has produced more promising results on other tasks [5, 6, 10, 11]. Combinations of di erent approaches for improving performance often work better than each approach alone [11] In particular, Juill e and Pollack [7, 8] recently investigated a combination of coevolution and resource sharing in evolving cellular automata (CAs) to perform a density classi cation ....

H. Juille and J. B. Pollack. Dynamics of co-evolutionary learning. In P. Maes, M. J. Mataric, J.-A. Meyer, J. Pollack, and S. W. Wilson, editors, From Animals to Animats 4: Proceedings of the Fourth International Conference on Simulation of Adaptive Behavior, Cambridge, MA, 1996. MIT Press.

....programming standpoint, the fact that the robot players have evolved to embody a robust set of strategies, capable of overcoming a wide range of human behaviours, is a powerful result. We have been studying co evolutionary learning environments in several contexts [Blair and Pollack, 1997,Juill e and Pollack, 1996,Ficici and Pollack, 1998] trying to understand the reasons why this paradigm works very well for some tasks [Hillis, 1992,Sims, 1995,Tesauro, 1995] but poorly for others. In particular, we have developed a minimalist co evolutionary learning method that consists of a neural network which ....

Juill'e, H. and Pollack, J. B. (1996). Dynamics of coevolutionary learning. In Proc. of SAB-4, pages 526--534. MIT Press.

....an evolutionary programming standpoint, the fact that the GP players have evolved to embody a robust set of strategies, capable of overcoming a wide range of human behaviours, is noteworthy. We have been studying co evolutionary learning environments in several contexts [Blair and Pollack, 1997,Juill e and Pollack, 1996,Ficici and Pollack, 1998] trying to understand the reasons why this paradigm works very well for some tasks [Hillis, 1992,Sims, 1995,Tesauro, 1995] but poorly for others. In particular, we have developed a minimalist co evolutionary learning method that consists of a neural network which ....

Juill'e, H. and Pollack, J. B. (1996). Dynamics of coevolutionary learning. In Proc. of SAB-4, pages 526--534. MIT Press.

....Finally, co evolution could be used to let the learning set coevolve with the animat population, so as to propose the most challenging environmental situations according to current population abilities. Such a possibility has been first proposed by Hillis [40] and further explored in [41] 42] [43]. The present work also demonstrates that, among the different paradigms that have been used to evolve the control architecture of an animat e.g. Lisp functions [21] 44] logic trees [45] 46] classifier systems [47] recurrent artificial neural networks exhibit several specific and ....

H. Juille and J. B. Pollack, "Dynamics of co-evolutionary learning," in From Animals to Animats 4. Proceedings of the Fourth International Conference on Simulation of Adaptive Behavior (P. Maes, M. J. Mataric, J.-A. Meyer, J. B. Pollack, and S. W. Wilson, eds.), pp. 526--534, The MIT Press/Bradford Books, Cambridge, MA, 1996.

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H. Juill and J. B. Pollack. Dynamics of co-evolutionary learning. In Proceedings of the Fourth International Conference on Simulation of Adaptive Behavior, pages 526--534. MIT Press, 1996.

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H. Juill and J. B. Pollack. Dynamics of co-evolutionary learning. In Proceedings of the Fourth International Conference on Simulation of Adaptive Behavior, pages 526--534. MIT Press, 1996. 16

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H. Juill and J. B. Pollack. Dynamics of co-evolutionary learning. In Proceedings of the Fourth International Conference on Simulation of Adaptive Behavior, pages 526--534. MIT Press, 1996.

.... industry [33] The second is rapid, one off prototyping and manufacture, which is proceeding from 3D plastic layering to stronger composite and metal (sintering) technology [7] The third is our understanding of the dynamics of coevolutionary learning in the selforganization of complex systems [1, 8, 19, 30]. 3 2 Coevolution Coevolution, when successful, dynamically creates a series of learning environments each slightly more complex than the last, and a series of learners which are tuned to adapt in those environments. Sims work [32] on body brain coevolution and the more recent Framsticks ....

H. Juill and J. B. Pollack. Dynamics of co-evolutionary learning. In Proceedings of the Fourth International Conference on Simulation of Adaptive Behavior, pages 526--534. MIT Press, 1996.

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Juille, H., Pollack, J.B.: Dynamics of co-evolutionary learning. In: Proceedings of the Fourth International Conference on Simulation of Adaptive Behavior, MIT Press (1996) 526-534

....generation is started. To start a new generation, the 100 current robots are sorted by fitness. The worst 10 are eliminated and replaced by 10 fresh robots, supplied by the background process. A new generation begins. The fitness of robots is a shared fitness measure designed to promote speciation[2, 9] by giving points for doing better than average against a human player, and negative points for doing worse than average. For each robot r, the fitness is calculated as (1) where lost(h,r) is the number of games lost by each human opponent h against r, played(h,r) is the total number of games ....

Juillé, H. and Pollack, J. (1996) Dynamics of Co-evolutionary Learning. Proceedings of the Fourth International Conference on Simulation of Adaptive Behavior. MIT Press.

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H. Juille and J. B. Pollack. Dynamics of co-evolutionary learning. In Proceedings of the Fourth International Conference on Simulation of Adaptive Behavior, pages 526--534. MIT Press, 1996.

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