Titlebook: Reasoning Web. Causality, Explanations and Declarative Knowledge; 18th International S Leopoldo Bertossi,Guohui Xiao Textbook 2023 The Edit

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0302-9743 table Probabilistic Circuits; From Statistical Relational to Neural Symbolic Artificial Intelligence; Building Intelligent Data Apps in Rel978-3-031-31413-1978-3-031-31414-8Series ISSN 0302-9743 Series E-ISSN 1611-3349

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Textbook 2023erest to Semantic Web and Linked Data applications. It is primarily intended for postgraduate students, postdocs, young researchers, and senior researchers wishing to deepen their knowledge. As in the previous years, lectures in the summer school were given by a distinguished group of expert lecture

改進(jìn)

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Cross-Modal Knowledge Discovery, Inference, and Challenges,odal knowledge have become the core technologies of the academic and industrial concern. This tutorial focuses on the state of the art of cross-modal knowledge discovery and inference and presents future research opportunities and challenges.

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,Attribution-Scores and?Causal Counterfactuals as?Explanations in?Artificial Intelligence,g that are based on attribution-scores, and counterfactuals as found in the area of causality. We elaborate on the importance of logical reasoning when dealing with counterfactuals, and their use for score computation.

金絲雀

0302-9743 of the Reasoning Web Summer School is to disseminate recent advances on reasoning techniques and related issues that are of particular interest to Semantic Web and Linked Data applications. It is primarily intended for postgraduate students, postdocs, young researchers, and senior researchers wishi

帳單

,Attribution-Scores and?Causal Counterfactuals as?Explanations in?Artificial Intelligence,ferring to origins and connections of and among different approaches. We describe in simple terms, explanations in data management and machine learning that are based on attribution-scores, and counterfactuals as found in the area of causality. We elaborate on the importance of logical reasoning whe

衰弱的心

,Logic-Based Explainability in?Machine Learning,come pervasive in a wide range of practical uses, including many that directly affect humans. Unfortunately, the operation of the most successful ML models is incomprehensible for human decision makers. As a result, the use of ML models, especially in high-risk and safety-critical settings is not wi

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