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Titlebook: Artificial Neural Nets and Genetic Algorithms; Proceedings of the I George D. Smith,Nigel C. Steele,Rudolf F. Albrecht Conference proceedin

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A Modular Reinforcement Learning Architecture for Mobile Robot Controlns to enable the co-ordination of reinforcement learning across both modules successive time steps. The experiments reported explore the possibility that architectures based on this approach can support concurrent acquisition of different reactive navigation related competences while the robot pursues light-seeking goals.
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A Modular Neural Network Architecture with Additional Generalization Abilities for Large Input Vecton. The modular network is designed to combine two different approaches of generalization known from connectionist and logical neural networks; this enhances the generalization abilities of the network. The architecture introduced here is especially useful in solving problems with a large number of input attributes.
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Bernoulli Mixture Model of Experts for Supervised Pattern Classificationatistical solutions. Performances can be largely improved if we introduce prior knowledge in network architectures. If the real problem has an obvious decomposition, then it may be possible to design a network architecture by hand. Unfortunately, this is not always possible.
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Fuzzy-tuned Stochastic Scanpaths for AGV Visionmentation on a microcontroller is developed by using stochastic transition matrices. The results presented show the success of the technique in maintaining interest in objects previously located within the environment, locating new objects in an environment and making a compromise between the two.
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