Titlebook: Women in Computational Intelligence; Key Advances and Per Alice E Smith Book 2022 Springer Nature Switzerland AG 2022 Women in science and

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Design and Validation of a Mini-Game for Player Motive Profilingthe fly or model the player population. One emerging use of computational intelligence in games is to improve player experience by tailoring a game to features of a player’s profile. To do this, we must first be able to model individual player’s profiles, of which one aspect is player motivation. Th

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Linguistic Intelligence as a Base for Computing Reasoningeep information clues about the internal and external environment of the individual. The exact nature of the reasoning performed by humans is not yet fully understood. This chapter revisits part of the author’s research effort to envision how human reasoning would be technically evaluated, parameter

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Corporeal

Reinforcement Learning Control by Direct Heuristic Dynamic Programming) method. Over the past several years, new analysis and synthesis of the dHDP as a reinforcement learning controller as well as impressive applications of the dHDP have emerged. In this chapter we provide a summary on how the dHDP works, what analytical properties it possesses, and how it was applie

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Distributed Machine Learning in Energy Management and Control in Smart Gridwarming, which has resulted in governmental policies encouraging their adoption. This growth is also due to significant improvements and cost reductions in various DER technologies. However, the real benefits of increased integration of DERs may not be fully appreciated unless proper control strateg

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Fuzzy Multilayer Perceptrons for Fuzzy Vector Regressionorithm that can deal with fuzzy inputs and fuzzy outputs. The optimal weights and biases are found by the cuckoo search algorithm. We show how this algorithm can be used in the regression research area using three real data sets, the yacht hydrodynamics data set, the energy efficiency data set, and

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Generalisation in Genetic Programming for Symbolic Regression: Challenges and Future Directionsming is an attractive and leading technique for symbolic regression, since it does not require any predefined model structure and has a flexible representation. However, genetic-programming-based symbolic regression (GPSR) often has a poor generalisation ability that hampers its applications to scie