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

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书目名称Artificial Intelligence XXXVI影响因子(影响力)




书目名称Artificial Intelligence XXXVI影响因子(影响力)学科排名




书目名称Artificial Intelligence XXXVI网络公开度




书目名称Artificial Intelligence XXXVI网络公开度学科排名




书目名称Artificial Intelligence XXXVI被引频次




书目名称Artificial Intelligence XXXVI被引频次学科排名




书目名称Artificial Intelligence XXXVI年度引用




书目名称Artificial Intelligence XXXVI年度引用学科排名




书目名称Artificial Intelligence XXXVI读者反馈




书目名称Artificial Intelligence XXXVI读者反馈学科排名

Conflict

Needlework

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

SHRIK

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

极肥胖

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

功多汁水

anthesis

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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