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Titlebook: Inductive Logic Programming; 29th International C Dimitar Kazakov,Can Erten Conference proceedings 2020 Springer Nature Switzerland AG 2020

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發(fā)表于 2025-3-21 16:05:43 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Inductive Logic Programming
副標(biāo)題29th International C
編輯Dimitar Kazakov,Can Erten
視頻videohttp://file.papertrans.cn/464/463888/463888.mp4
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Inductive Logic Programming; 29th International C Dimitar Kazakov,Can Erten Conference proceedings 2020 Springer Nature Switzerland AG 2020
描述.This book constitutes the refereed conference proceedings of the 29th International Conference on Inductive Logic Programming, ILP 2019, held in Plovdiv, Bulgaria, in September 2019...The 11 papers presented were carefully reviewed and selected from numerous submissions. Inductive Logic Programming (ILP) is a subfield of machine learning, which originally relied on logic programming as a uniform representation language for expressing examples, background knowledge and hypotheses. Due to its strong representation formalism, based on first-order logic, ILP provides an excellent means for multi-relational learning and data mining, and more generally for learning from structured data..
出版日期Conference proceedings 2020
關(guān)鍵詞artificial intelligence; computer programming; computer systems; data mining; formal logic; inductive log
版次1
doihttps://doi.org/10.1007/978-3-030-49210-6
isbn_softcover978-3-030-49209-0
isbn_ebook978-3-030-49210-6Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer Nature Switzerland AG 2020
The information of publication is updating

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Rapid Restart Hill Climbing for Learning Description Logic Concepts,r expansion by traversing the search tree in a hill climbing manner and rapidly restarts with one-step backtracking after each expansion. We provide an implementation of RRHC in the DL-Learner framework and compare its performance with CELOE using standard benchmarks.
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Towards Meta-interpretive Learning of Programming Language Semantics, scenario, including abstracting over function symbols, nonterminating examples, and learning non-observed predicates, and propose extensions to Metagol helpful for overcoming these challenges, which may prove useful in other domains.
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Towards an ILP Application in Machine Ethics,on approach relies on the non-monotonic features of Answer Set Programming (ASP) and applies ILP. The approach is illustrated by means of examples taken from the preliminary tests conducted with a couple of state-of-the-art ILP algorithms for learning ASP rules.
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Learning Logic Programs from Noisy State Transition Data,sed to understand the underlying model. In this paper, we propose a Differentiable Learning from Interpretation Transition (.-LFIT) algorithm, that can simultaneously output logic programs fully explaining the state transitions, and also learn from data containing noise and error.
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0302-9743 xamples, background knowledge and hypotheses. Due to its strong representation formalism, based on first-order logic, ILP provides an excellent means for multi-relational learning and data mining, and more generally for learning from structured data..978-3-030-49209-0978-3-030-49210-6Series ISSN 0302-9743 Series E-ISSN 1611-3349
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