Titlebook: Artificial Intelligence and Machine Learning; 33rd Benelux Confere Luis A. Leiva,Cédric Pruski,Christoph Schommer Conference proceedings 20

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A Bayesian Framework for?Evaluating Evolutionary Artpresentation. These images are then used in an ATT in which . subjects participated. The results indicate a weak preference for the alternative hypothesis, showing that the human- and computer-generated images can not reliably be distinguished. We sketch future applications of the framework, such as

DEMN

騷動(dòng)

Verbalizing but Not Just Verbatim Translations of Ontology Axioms the ones with high expressivity and specificity. This technique utilizes a predefined set of rules which are applied repeatedly on the restrictions associated with the individuals (and the concepts) to obtain a refined set of restrictions, guaranteed to be semantically equivalent to the original re

CAB

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MoveRL: To?a?Safer Robotic Reinforcement Learning Environmentxperimental results show that a standard PPO agent is able to control a simulated commercial robot arm in an environment with moving obstacles, while almost perfectly avoiding collisions even in the early stages of learning. We also show that the use of MoveIt slightly increases the sample-efficienc

難解

https://doi.org/10.1007/978-3-030-93842-0artificial intelligence; Bayesian Learning; Causal Learning; communication systems; Computational Creati

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978-3-030-93841-3Springer Nature Switzerland AG 2022

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