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Titlebook: Automated Deduction – CADE 27; 27th International C Pascal Fontaine Conference proceedings 2019 Springer Nature Switzerland AG 2019 artific

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發(fā)表于 2025-3-21 17:19:16 | 只看該作者 |倒序瀏覽 |閱讀模式
期刊全稱Automated Deduction – CADE 27
期刊簡稱27th International C
影響因子2023Pascal Fontaine
視頻videohttp://file.papertrans.cn/167/166265/166265.mp4
學科分類Lecture Notes in Computer Science
圖書封面Titlebook: Automated Deduction – CADE 27; 27th International C Pascal Fontaine Conference proceedings 2019 Springer Nature Switzerland AG 2019 artific
影響因子.This book constitutes the proceeding of the 27th International Conference on Automated Deduction, CADE 27, held in Natal, Brazil, in August 2019.. The 27 full papers and 7 system descriptions presented were carefully reviewed and selected from 65 submissions. CADE is the major forum for the presentation of research in all aspects of automated deduction, including foundations, applications, implementations, and practical experience...?.
Pindex Conference proceedings 2019
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書目名稱Automated Deduction – CADE 27影響因子(影響力)




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Extending SMT Solvers to Higher-Order Logic,contrast, the extension of SMT solvers to higher-order logic (HOL) is mostly unexplored. We propose a pragmatic extension for SMT solvers to support HOL reasoning natively without compromising performance on FOL reasoning, thus leveraging the extensive research and implementation efforts dedicated t
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Superposition with Lambdas,es Booleans. The inference rules work on .-equivalence classes of .-terms and rely on higher-order unification to achieve refutational completeness. We implemented the calculus in the Zipperposition prover and evaluated it on TPTP and Isabelle benchmarks. The results suggest that superposition is a
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Restricted Combinatory Unification,an carry out some higher-order reasoning. In his 1991 paper, Dougherty proposed a combinatory unification algorithm for higher-order logic. The algorithm removes the need to deal with .-binders and .-renaming, making it attractive to implement in first-order provers. However, since publication it ha
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ENIGMA-NG: Efficient Neural and Gradient-Boosted Inference Guidance for E,h. Unlike in the first ENIGMA implementation where a fast linear classifier is trained and used together with manually engineered features, we have started to experiment with more sophisticated state-of-the-art machine learning methods such as gradient boosted trees and recursive neural networks. In
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