Answer Set programming is widely applied research area for knowledge representation and for solving industrial domains. One of the challenges of this formalism focuses on the so-called grounding bottleneck, which addr...
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Answer set programming (ASP) is a popular problem solving paradigm with applications in planning and configuration. In practice, the number of answer sets can be overwhelmingly high, which naturally causes interest in...
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In this paper, we deal with inconsistency resolution in qualitative constraint networks (QCN). This type of networks allows one to represent and reason about spatial or temporal information in a natural, human-like ma...
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Preparing healthy and delicious meals requires knowledge about the process, nutritional properties of the ingredients, and their combinations. When we face the need for substitution in a dish, e.g., for allergic or di...
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2022 marks the 50th anniversary of the logic programming language Prolog. In this regard, the authors will join the international initiative known as 'Prolog Education and Thinking'. The initiative aims to acq...
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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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Theory of strongly equivalent transformations is an essential part of the methodology of representing knowledge in answer set programming. Strong equivalence of two programs can be sometimes characterized as the possi...
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logical English (LE) is a natural language syntax for pure Prolog and other logic programming languages, such as ASP and s(CASP). Its main applications until now have been to explore the representation of a wide range...
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Answer-Set programming (ASP) has seen tremendous progress over the last two decades and is nowadays successfully applied in many real-world domains. However, for certain problems, the well-known ASP grounding bottlene...
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Stochastic logic programming (Slp) and Distributional logic programming (Dlp) are two closely related probabilistic logic programming formalisms that have been previously studied in the context of machine learning. Th...
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