What is it about?
DLV is a powerful system for Knowledge Representation and Reasoning which supports Answer Set Programming (ASP) – a logic-based programming paradigm for solving problems in a fully declarative way. DLV is currently widely used in academy, and, importantly, it has been fruitfully employed in many relevant industrial applications. Similarly to the other main-stream ASP systems, while processing an input program, in a first phase of the computation DLV eliminates the variables, thus generating a ground program which is semantically equivalent to the original one, but significantly smaller than the Herbrand Instantiation, in general. This phase, called ‘grounding’, plays a key role for the successful deployment in real-world contexts. In this work we present ℐ-DLV, a brand new version of the intelligent grounder of DLV . While relying on the solid theoretical foundations of its predecessor, it has been completely redesigned and re-engineered, both in algorithms and data structures; it now features full support to ASP-Core-2 standard language, increased flexibility and customizability, significantly improved performance, and an extensible design that eases the incorporation of language updates and optimization techniques. ℐ-DLV results in a stable and efficient ASP instantiator, that turns out to be a full-fledged deductive database system. We describe here the main features of ℐ-DLV and present the results of proper experimental activities for assessing both its applicability and performance.
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Why is it important?
It is a step towards the adoption of the standard input language for ASP systems and a solid contribution as a logic-based artificial intelligence system, as well as a deductive database system. Furthermore, it is very customizable, thus helping experts at fine tuning solutions geared at real-world problems, and beginners at learning and experimenting with Answer Set Programming and Artificial Intelligence.
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This page is a summary of: I-DLV: The new intelligent grounder of DLV, Intelligenza Artificiale The international journal of the AIxIA, April 2017, IOS Press,
DOI: 10.3233/ia-170104.
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