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Titlebook: Artificial Intelligence Logic and Applications; The 3rd Internationa Songmao Zhang,Yonggang Zhang Conference proceedings 2023 The Editor(s)

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楼主: purulent
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Evidence-Based Argumentation and Its Incremental Semanticsevidence and can then defeat their opponents. Moreover, the incremental semantics of the framework explicate the fallibility and cumulative nature of evidence. It largely increases the expressive power of formal argumentation.
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An Abstraction Neural Network Generator for Efficient Formal Verificationstance-Based Merging (DBM) operator to optimize the abstract operation. Also, we propose a Dynamic-Merging Abstraction (DMA) algorithm to heuristically merge the network. Experimental results show that our method outperforms other state-of-the-art methods in terms of output precision.
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A Diagnostic Model Generation Method Based on Clustering to the diagnostic model to speed up the diagnosis. The heuristics information is obtained from the clustering. In the online diagnosis, once the undefined behavior is found, the model is reconstructed according to the classification and order relationship of the undefined behavior in the existing cluster, and the possible diagnoses is obtained.
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Evidence-Based Argumentation and Its Incremental Semantics work, the crucial role that evidence plays has not been adequately captured. To address this issue, this paper proposes an extension of Dung’s abstract argumentation framework that includes a set of evidence and the concept of evidential support. Under this framework, arguments derive support from
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Granular Computing Measures for the Classical Formal Concepts and Intuitionistic Fuzzy Formal Concepst fires. Constructing periodical timeslots or spatial hierarchies like country, state, region etc. approaches are used to explore knowledge about event occurrences for public security or safety purposes. Granular Computing approach is used with Formal Concept Analysis to discover knowledge in such
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