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A System for Probabilistic Inductive Answer Set Programming

  • Matthias NicklesEmail author
  • Alessandra Mileo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9310)

Abstract

We describe a prototypical software framework for probabilistic inductive logic programming which supports the seamless combination of non-monotonic reasoning, probabilistic inference and parameter learning. While building upon existing as well as new approaches to probabilistic Answer Set Programming, our framework distinguishes itself from related works by placing virtually no restrictions on the annotation of knowledge with probabilities. User-configurable algorithms provide for general as well as specialized, scalable approaches to inference and parameter learning, allowing for adaptability with regard to complex reasoning and weight learning tasks.

Keywords

Inference Algorithm Parameter Learning Inductive Logic Programming Probabilistic Inference Stable Model Semantic 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Insight Centre for Data AnalyticsNational University of Ireland, GalwayGalwayIreland

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