17 resultados para Artificial Intelligence, Constraint Programming, set variables, representation


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Model finders are very popular for exploring scenarios, helping users validate specifications by navigating through conforming model instances. To be practical, the semantics of such scenario exploration operations should be formally defined and, ideally, controlled by the users, so that they are able to quickly reach interesting scenarios. This paper explores the landscape of scenario exploration operations, by formalizing them with a relational model finder. Several scenario exploration operations provided by existing tools are formalized, and new ones are proposed, namely to allow the user to easily explore very similar (or different) scenarios, by attaching preferences to model elements. As a proof-of-concept, such operations were implemented in the popular Alloy Analyzer, further increasing its usefulness for (user-guided) scenario exploration.

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Temporal logics targeting real-time systems are traditionally undecidable. Based on a restricted fragment of MTL-R, we propose a new approach for the runtime verification of hard real-time systems. The novelty of our technique is that it is based on incremental evaluation, allowing us to e↵ectively treat duration properties (which play a crucial role in real-time systems). We describe the two levels of operation of our approach: offline simplification by quantifier removal techniques; and online evaluation of a three-valued interpretation for formulas of our fragment. Our experiments show the applicability of this mechanism as well as the validity of the provided complexity results.