Recent studies have demonstrated that compilation-based techniques can be beneficial for evaluating Datalog and ASP programs. In this paper, we develop a compiler that is able to generate solvers for normal logic prog...
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Answer Set programming (ASP) is a well-known AI formalism. Traditional ASP systems, that follow the 'ground&solve' approach, are intrinsically limited by the so-called grounding bottleneck. Basically, the ...
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The scheduling of periodic treatments consists of planning a care path over a period of several weeks, in which patients have to perform different treatments respecting a certain periodicity. Treatments must be assign...
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ILASP (Inductive Learning of Answer Set Programs) is a logic-based machine learning system. It makes use of existing knowledge base, containing anything known before the learning starts or even previously learned rule...
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We present StHorn, a novel technique for solving the satisfiability problem of CHCs, which works lazily and incrementally and is guided by the structure of the set of CHCs. Our technique is driven by the idea that a s...
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The importance of enhancing the sustainability of buildings has been sharply growing over the last few years. One of the most significant aspects in this regard is social sustainability, as it encompasses the well-bei...
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The λDat calculus brings together the power of functional and declarative logic programming in one language. In λDat, Datalog constraints are first-class values that can be constructed, passed around as arguments, r...
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Communicating Datalog Programs (CDPs) are a distributed computing model grounded on logic programming, where networks of nodes perform Datalog-like computations, leveraging also on information coming from incoming mes...
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The promise of automation of legal reasoning is developing technology that reduces human time required for legal tasks or that improves human performance on such tasks. In order to do so, different methods and systems...
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The promise of automation of legal reasoning is developing technology that reduces human time required for legal tasks or that improves human performance on such tasks. In order to do so, different methods and systems based on logic programming were developed. However, in order to apply such methods on legal data, it is necessary to provide an interface between human users and the legal reasoning system, and the most natural interface in the legal domain is natural language, in particular, written text. In order to perform reasoning in written text using logic programming methods, it is then necessary to map expressions in text to atoms and predicates in the formal language, a task referred generally as information extraction. In this work, we propose a new dataset for the task of information extraction, in particular event extraction, in court decisions, focusing on contracts. Our dataset captures contractual relations and events that affect them in some way, such as negotiations preceding a (possible) contract, the execution of a contract, or its termination. We conducted text annotation with law students and graduates, resulting in a dataset with 207 documents, 3934 sentences, 4440 entities, and 1794 events. We describe here this resource, the annotation process, its evaluation with inter-annotator agreement metrics, and discuss challenges during the development of this resource and for the future. 2023 Copyright for this paper by its authors.
We present an approach to non-deterministic planning under full observability via Answer Set programming. The technique can synthesise compact policies, handle both fair and unfair actions simultaneously, and readily ...
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