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Titlebook: Artificial Intelligence XXXVI; 39th SGAI Internatio Max Bramer,Miltos Petridis Conference proceedings 2019 Springer Nature Switzerland AG 2

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期刊全稱Artificial Intelligence XXXVI
期刊簡稱39th SGAI Internatio
影響因子2023Max Bramer,Miltos Petridis
視頻videohttp://file.papertrans.cn/163/162162/162162.mp4
學科分類Lecture Notes in Computer Science
圖書封面Titlebook: Artificial Intelligence XXXVI; 39th SGAI Internatio Max Bramer,Miltos Petridis Conference proceedings 2019 Springer Nature Switzerland AG 2
影響因子.This book constitutes the proceedings of the 39th SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, AI 2019, held in Cambridge, UK, in December 2019..The 29 full papers and 14 short papers presented in this volume were carefully reviewed and selected from 49 submissions. The volume includes technical papers presenting new and innovative developments in the field? as well as application papers? presenting innovative applications of AI techniques in a number of subject domains. The papers are organized in the following topical sections: machine learning; knowledge discovery and data mining; agents, knowledge acquisition and ontologies; medical applications; applications of evolutionary algorithms; machine learning for time series data; applications of machine learning; and knowledge acquisition.
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Towards Model-Based Reinforcement Learning for Industry-Near Environmentslittle visible information. Rainbow (Q-Learning) and PPO (Policy Optimisation) have shown outstanding performance in a variety of tasks, including Atari 2600, MuJoCo, and Roboschool test suite. Although these algorithms are fundamentally different, both suffer from high variance, low sample efficien
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Stepwise Evolutionary Learning Using Deep Learned Guidance Functions evolutionary algorithms. A new form of LGF is introduced, based on deep neural network learning, and it is shown how this can be used as a fitness function. This is applied to a test problem: unscrambling the Rubik’s Cube. Comparisons are made with a previous LGF approach based on random forests, a
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Monotonicity Detection and Enforcement in Longitudinal Classificationeful knowledge based on the changes of the data over time. Monotonic relations often occur in real-world data and need to be preserved in data mining models in order for the models to be acceptable by users. We propose a new methodology for detecting monotonic relations in longitudinal datasets and
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Understanding Structure of Concurrent Actionsre in the action space can improve sample efficiency during exploration. To show this we focus on concurrent action spaces where the RL agent selects multiple actions per timestep. Concurrent action spaces are challenging to learn in especially if the number of actions is large as this can lead to a
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Ontology-Driven, Adaptive, Medical Questionnaires for Patients with Mild Learning Disabilities inequalities and worse outcomes than the general population. Primary care practitioners are often the first port-of-call for medical consultations, and one issue faced by LD patients in this context is the very limited time available during consultations - typically less than ten minutes. In order
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