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Titlebook: Coalgebraic Methods in Computer Science; 17th IFIP WG 1.3 Int Barbara König,Henning Urbat Conference proceedings 2024 IFIP International Fe

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书目名称Coalgebraic Methods in Computer Science
副标题17th IFIP WG 1.3 Int
编辑Barbara König,Henning Urbat
视频video
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Coalgebraic Methods in Computer Science; 17th IFIP WG 1.3 Int Barbara König,Henning Urbat Conference proceedings 2024 IFIP International Fe
描述.This book constitutes the post-conference proceedings of the 17th International Workshop on Coalgebraic Methods in Computer Science, CMCS 2024, colocated with ETAPS 2024, held in Luxembourg in April 2024...The 10 papers included in these proceedings were carefully reviewed and selected from 15 submissions. The papers cover a wide range of topics on theory, logics, and applications of coalgebras..
出版日期Conference proceedings 2024
关键词Behavioural equivalences; automata theory; boolean algebra; boolean functions; data types; coinductive de
版次1
doihttps://doi.org/10.1007/978-3-031-66438-0
isbn_softcover978-3-031-66437-3
isbn_ebook978-3-031-66438-0Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightIFIP International Federation for Information Processing 2024
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https://doi.org/10.1007/978-3-030-00362-3ed in categorical terms. A key contribution of our paper is to identify the categorical essence of the standard encoding of CTL into the modal mu-calculus. This categorical perspective also explains the absence of a similar encoding of PCTL (Probabilistic CTL) into the probabilistic mu-calculus.
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,Coalgebraic CTL: Fixpoint Characterization and Polynomial-Time Model Checking,ed in categorical terms. A key contribution of our paper is to identify the categorical essence of the standard encoding of CTL into the modal mu-calculus. This categorical perspective also explains the absence of a similar encoding of PCTL (Probabilistic CTL) into the probabilistic mu-calculus.
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https://doi.org/10.1007/978-3-319-40688-6ised by a positive number (“look-ahead bound”), and forms a generative spectrum governed by the look-ahead bound. We analyse the complexity of determining preorder-constrained similarity, and show that preorder-constrained simulation can be enhanced by the so-called up-to technique.
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,Explicit Hopcroft’s Trick in Categorical Partition Refinement, exposes a concrete tree structure to which Hopcroft’s inequality readily applies. It is notable that our fibrational framework accommodates such algorithmic analysis on the categorical level of abstraction.
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