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Found 11 papers, 3 papers with code
Extending Answer Set Programming with Rational Numbers
Answer Set Programming (ASP) is a widely used declarative programming paradigm that has shown great potential in solving complex computational problems.
Rethinking Answer Set Programming Templates
In imperative programming, the Domain-Driven Design methodology helps in coping with the complexity of software development by materializing in code the invariants of a domain of interest.
I-DLV-sr: A Stream Reasoning System based on I-DLV
We introduce a novel logic-based system for reasoning over data streams, which relies on a framework enabling a tight, fine-tuned interaction between Apache Flink and the I^2-DLV system.
A Machine Learning guided Rewriting Approach for ASP Logic Programs
In particular, given an ASP program and a set of input facts, our approach chooses whether and how to rewrite input rules based on a set of features measuring their structural properties and domain information.
Incremental maintenance of overgrounded logic programs with tailored simplifications
The repeated execution of reasoning tasks is desirable in many applicative scenarios, such as stream reasoning and event processing.
DaRLing: A Datalog rewriter for OWL 2 RL ontological reasoning under SPARQL queries
The W3C Web Ontology Language (OWL) is a powerful knowledge representation formalism at the basis of many semantic-centric applications.
Precomputing Datalog evaluation plans in large-scale scenarios
With the more and more growing demand for semantic Web services over large databases, an efficient evaluation of Datalog queries is arousing a renewed interest among researchers and industry experts.
Incremental Answer Set Programming with Overgrounding
In this context, we propose an incremental grounding approach for the answer set semantics.
Enhancing magic sets with an application to ontological reasoning
It turns out that the new version of magic sets is closed for Datalog programs with stratified negation and aggregations, which is very convenient to obtain efficient computation of the stable model of the rewritten program.
Optimizing Answer Set Computation via Heuristic-Based Decomposition
Furthermore, we define a set of new heuristics tailored at optimizing grounding, one of the main phases of the ASP computation; we use them in order to implement the approach into the ASP system DLV, in particular into its grounding subsystem I-DLV, and carry out an extensive experimental activity for assessing the impact of the proposal.
A Framework for Easing the Development of Applications Embedding Answer Set Programming
Answer Set Programming (ASP) is a well-established declarative problem solving paradigm which became widely used in AI and recognized as a powerful tool for knowledge representation and reasoning (KRR), especially for its high expressiveness and the ability to deal also with incomplete knowledge.
