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Titlebook: Dynamic Logic. New Trends and Applications; 5th International Wo Nina Gierasimczuk,Fernando R. Velázquez-Quesada Conference proceedings 202

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發(fā)表于 2025-3-26 23:49:13 | 只看該作者
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發(fā)表于 2025-3-27 05:12:41 | 只看該作者
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發(fā)表于 2025-3-27 08:47:56 | 只看該作者
,A Spatial Logic with?Time and?Quantifiers,er SLCS, the Spatial Logic for Closure Spaces, recently used for describing features of images and video frames. We extend SLCS in two directions. We first introduce first-order quantifiers, ranging on both individuals and atomic propositions. We then equip the logic with temporal operators, and pro
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發(fā)表于 2025-3-27 12:48:43 | 只看該作者
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發(fā)表于 2025-3-27 16:46:36 | 只看該作者
,Axiomatization of?Hybrid Logic of?Link Variations, global operators, the logic . of global link variations based on hybrid logic . is introduced, which involves global link cutting, adding and rotating simultaneously. A Hilbert-style calculus . is provided. By constructing families of canonical models inductively, we prove that the calculus . is so
36#
發(fā)表于 2025-3-27 18:16:47 | 只看該作者
,Kleene Algebra of?Weighted Programs with?Domain, represent optimization problems and, in general, programs whose execution traces carry some sort of weight. Batz et al.?show that a weighted version of Dijkstra’s weakest precondition operator can be used to reason about the competitive ratios of weighted programs. In this paper we study a proposit
37#
發(fā)表于 2025-3-28 00:23:01 | 只看該作者
,Automated Quantum Program Verification in?Dynamic Quantum Logic,mated approach to quantum program verification at the cost of simplifying DQL to Basic Dynamic Quantum Logic (BDQL). We first formalize quantum states, quantum gates, and projections in bra-ket notation and use a set of laws from quantum mechanics and matrix operations to reason on quantum computati
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發(fā)表于 2025-3-28 04:37:23 | 只看該作者
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發(fā)表于 2025-3-28 07:57:49 | 只看該作者
,Learning by?Intervention in?Simple Causal Domains,servable and that the underlying causal structure is of a simple nature. We adapt the frameworks of the (epistemic) causal models from [., .], and propose a model inspired by action learning [., .]. We present two learning methods, using formal and algorithmic approaches. Our learning agents infer d
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發(fā)表于 2025-3-28 10:39:03 | 只看該作者
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