The left-corner transformation (Rosenkrantz and Lewis, 1970) is used to remove left recursion from context-free grammars, which is an important step towards making the grammar parsable top-down with simple techniques....
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We recently proposed Acceleration Driven Clause Learning (ADCL), a novel calculus to analyze satisfiability of Constrained Horn Clauses (CHCs). Here, we adapt ADCL to transition systems and introduce ADCL-NT, a varian...
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Rule-based reasoning is an essential part of human intelligence prominently formalized in artificial intelligence research via logic programs. Describing complex objects as the composition of elementary ones is a comm...
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The author has recently introduced the sequential composition of propositional logic programs. This paper studies composition in the Krom fragment from an algebraic point of view. In a broader sense, this paper is a f...
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The rise of powerful AI technology for a range of applications that are sensitive to legal, social, and ethical norms demands decision-making support in presence of norms and regulations. Normative reasoning is the re...
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Considering the fatality of phishing attacks that are emphasized by many organizations, the inductive learning approach using reported malicious URLs has been verified in the field of deep learning. However, the deep ...
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ISBN:
(纸本)9781728176055
Considering the fatality of phishing attacks that are emphasized by many organizations, the inductive learning approach using reported malicious URLs has been verified in the field of deep learning. However, the deep learning-based method mainly focused on the fitting of a classification task via historical URL observation shows a limitation of recall due to the characteristics of zero-day attack. In order to model the nature of a zero-day phishing attack in which URL addresses are generated and discarded immediately, an approach that utilizes the expert knowledge is promising. We introduce the integration method of deep learning and logic programmed domain knowledge to inject the real-world constraints. We design neural and logic classifiers and propose the joint learning method of each component based on the traditional neuro-symbolic integration. Extensive experiments on three real-world datasets consisting of 222,541 URLs showed the highest recall among the latest deep learning methods, despite the hostile class-imbalanced condition. We demonstrate that the optimized weighting between neural and logic component has an effect of improving the recall over 3% compared to the existing methods.
Semi-autonomous driving, as it is already available today and will eventually become even more accessible, implies the need for driver and automation system to reliably work together in order to ensure safe driving. A...
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Recently, ABA Learning has been proposed as a form of symbolic machine learning for drawing Assumption-Based Argumentation frameworks from background knowledge and positive and negative examples. We propose a novel me...
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This system demonstration presents Nemo, a new logic programming engine with a focus on reliability and performance. Nemo is built for data-centric analytic computations, modelled in a fully declarative Datalog dialec...
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Answer Set programming (ASP) is a prominent rule-based language for knowledge representation and reasoning with roots in logic programming and non-monotonic reasoning. The aim to capture the essence of removing (ir)re...
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