Titlebook: Agents and Artificial Intelligence; 11th International C Jaap van den Herik,Ana Paula Rocha,Luc Steels Conference proceedings 2019 Springer

膽小鬼

https://doi.org/10.1007/978-1-349-27669-1 drift scenarios by providing the flexibility to consider at each step a portion of the data and derive clusters from it to be used subsequently to update the existing clustering solution. The proposed framework is built around the idea to model two clustering solutions as a bipartite graph, which g

Nmda-Receptor

The Intelligentsia in War and Revolution,nto it. Our research aims at a simple and practical Wide and Deep Reinforcement Learning framework to extend DRL algorithms by combining linear (wide) and non-linear (deep) methods. This framework can help to integrate expert knowledge or to fuse sensor information while at the same time improving t

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interlude

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deforestation

Harold G. Levine,L. L. Langness to solve. Especially in the high-dimensional case, a lack of derivatives entails additional challenges to heuristics. High-dimensionality leads to an exponential increase in search space size and tightens the problem of premature convergence. Parallelization for acceleration often involves domain-s

manifestation

https://doi.org/10.1007/978-1-349-21518-8show interest in new technologies and their possible applications for customers. German insurance companies generally are interested in improving their customer service and digitizing their business processes. In this work we investigate the potential use of conversational agents in insurance compan

Favorable

Lecture Notes in Computer Sciencehttp://image.papertrans.cn/a/image/151229.jpg

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disparage

978-3-030-37493-8Springer Nature Switzerland AG 2019