Titlebook: Applications of Evolutionary Computation; EvoApplications 2011 Cecilia Chio,Stefano Cagnoni,Georgios N. Yannakaki Conference proceedings 20

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Advanced Computer Human InteractionsBehaviour Trees, in order to create controllers for the Mario AI Benchmark. The results obtained reinforce the applicability of evolutionary programming systems to the development of artificial intelligence in games, and in dynamic systems in general, illustrating their viability as an alternative t

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Advanced Computer Human Interactionsular method for encoding intelligent behaviours in game is by scripting where the behaviours on the scene are predetermined. Many popular games have their game intelligence encoded in this manner. The application of machine learning techniques to learn non-player character behaviours is still being

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Advanced Computer Human Interactionsybrid solution to the segmentation problem. A linear filter composed of a Gaussian and a Laplacian of Gaussian filter is used to smooth the image, before applying a dynamic threshold to extract a rough segmentation. In parallel, a despeckle filter based on a Cellular Automata (CA) is used to remove

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https://doi.org/10.1007/978-3-030-10576-1(GMM) method are popular in image segmentation, but it is computationally difficult to find their globally optimal threshold values. Particle Swarm Optimisation (PSO) is an intelligent search method and has been widely used in many fields. However it is also easily trapped in local optima. In this p

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https://doi.org/10.1007/978-3-030-10576-1 accuracy of results. The Evolutionary Computation technique of Learning Classifier Systems (LCS) addresses such problems, but has not been applied previously to this domain. Instead, offline, supervised techniques on fixed data sets have been shown to be highly accurate. This paper shows that LCS e