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Found 10 papers, 2 papers with code
Data Augmentation: a Combined Inductive-Deductive Approach featuring Answer Set Programming
Although the availability of a large amount of data is usually given for granted, there are relevant scenarios where this is not the case; for instance, in the biomedical/healthcare domain, some applications require to build huge datasets of proper images, but the acquisition of such images is often hard for different reasons (e. g., accessibility, costs, pathology-related variability), thus causing limited and usually imbalanced datasets.
A Formal Comparison between Datalog-based Languages for Stream Reasoning (extended version)
The paper investigates the relative expressiveness of two logic-based languages for reasoning over streams, namely LARS Programs -- the language of the Logic-based framework for Analytic Reasoning over Streams called LARS -- and LDSR -- the language of the recent extension of the I-DLV system for stream reasoning called I-DLV-sr.
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.
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.
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.
Unfounded Sets and Well-Founded Semantics of Answer Set Programs with Aggregates
As a consequence, a well-founded semantics for general LPA programs that allows for tractable computation is unlikely to exist, which justifies the restriction on LPAma programs.
The DLV System for Knowledge Representation and Reasoning
As for problem solving, we provide a formal definition of its kernel language, function-free disjunctive logic programs (also known as disjunctive datalog), extended by weak constraints, which are a powerful tool to express optimization problems.
