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    Adaptive Peircean decision aid project summary assessments.

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    USING COEVOLUTION IN COMPLEX DOMAINS

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    Genetic Algorithms is a computational model inspired by Darwin's theory of evolution. It has a broad range of applications from function optimization to solving robotic control problems. Coevolution is an extension of Genetic Algorithms in which more than one population is evolved at the same time. Coevolution can be done in two ways: cooperatively, in which populations jointly try to solve an evolutionary problem, or competitively. Coevolution has been shown to be useful in solving many problems, yet its application in complex domains still needs to be demonstrated.Robotic soccer is a complex domain that has a dynamic and noisy environment. Many Reinforcement Learning techniques have been applied to the robotic soccer domain, since it is a great test bed for many machine learning methods. However, the success of Reinforcement Learning methods has been limited due to the huge state space of the domain. Evolutionary Algorithms have also been used to tackle this domain; nevertheless, their application has been limited to a small subset of the domain, and no attempt has been shown to be successful in acting on solving the whole problem.This thesis will try to answer the question of whether coevolution can be applied successfully to complex domains. Three techniques are introduced to tackle the robotic soccer problem. First, an incremental learning algorithm is used to achieve a desirable performance of some soccer tasks. Second, a hierarchical coevolution paradigm is introduced to allow coevolution to scale up in solving the problem. Third, an orchestration mechanism is utilized to manage the learning processes

    Co-evolution, determinism and robustness

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    Abstract. Robustness has long been recognised as a critical issue for coevolutionary learning. It has been achieved in a number of cases, though usually in domains which involve some form of non-determinism. We examine a deterministic domain � a pseudo real-time two-player game called Tron � and evolve a neural network player using a simple hillclimbing algorithm. The results call into question the importance of determinism as a requirement for successful co-evolutionary learning, and provide a good opportunity to examine the relative importance of other factors

    Co-evolution, Determinism and Robustness

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    . Robustness has long been recognised as a critical issue for coevolutionary learning. It has been achieved in a number of cases, though usually in domains which involve some form of non-determinism. We examine a deterministic domain -- a pseudo real-time two-player game called Tron -- and evolve a neural network player using a simple hillclimbing algorithm. The results call into question the importance of determinism as a requirement for successful co-evolutionary learning, and provide a good opportunity to examine the relative importance of other factors. Keywords: co-evolution, neural networks 1 Introduction. In 1982, Walt Disney Studios released a film called Tron which featured a game in a virtual world with two futuristic motorcycles running at constant speed, making only right angle turns and leaving solid wall trails behind them. As the game advanced, the arena filled with walls and eventually one opponent would die by crashing into a wall. This game became very popular and wa..
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