Titlebook: AIxIA 2020 – Advances in Artificial Intelligence; XIXth International Matteo Baldoni,Stefania Bandini Conference proceedings 2021 Springer

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In Defence of Design Patterns for AI Planning Knowledge Modelsfor better products and companies seeking to leverage their resources more efficiently have also been leading this push. In response to these market forces, we have recently seen a renewed and widely spoken about interest in the field of machine learning. At the cross-section of statistics, mathemat

火光在搖曳

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Zhening Li,Simai He,Shuzhong ZhangAI models. In this paper, we present the first version of ., a semantic-based explainable framework aiming to exploit semantic information for making black boxes more transparent. After a theoretical discussion, we show how this research direction is suitable and worthy of investigation by showing its application to a real-world use case.

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Melanoma

https://doi.org/10.1007/BFb0087392nchmarks with realistic parameters for schedule’s length up to the target 5-days planning. The results of our experiments show that ASP is a suitable methodology for solving also such enhanced problem.

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Discrete