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Metabolic Pathways as Temporal Logic Programs Read chapter Read first chapter

2016 | OriginalPaper | Chapter

A Tool for Probabilistic Reasoning Based on Logic Programming and First-Order Theories Under Stable Model Semantics

Author : Matthias Nickles

Published in: Logics in Artificial Intelligence

Publisher: Springer International Publishing

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Abstract

This System Description paper describes the software framework PrASP (“Probabilistic Answer Set Programming”). PrASP is both an uncertainty reasoning and machine learning software and a probabilistic logic programming language based on Answer Set Programming (ASP). Besides serving as a research software platform for non-monotonic (inductive) probabilistic logic programming, our framework mainly targets applications in the area of uncertainty stream reasoning. PrASP programs can consist of ASP (AnsProlog) as well as First-Order Logic formulas (with stable model semantics), annotated with conditional or unconditional probabilities or probability intervals. A number of alternative inference algorithms allow to attune the system to different task characteristics (e.g., whether or not independence assumptions can be made).

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Footnotes
2
PrASP’s default ASP grounder/solver Clingo also allows for function symbols, but for simplicity we ignore functions in the rest of this section.
 
4
Not related to the Iterative Refinement method in linear systems solving.
 
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Metadata
Title
A Tool for Probabilistic Reasoning Based on Logic Programming and First-Order Theories Under Stable Model Semantics
Author
Matthias Nickles
Copyright Year
2016
Book
Logics in Artificial Intelligence

Print ISBN: 978-3-319-48757-1

Electronic ISBN: 978-3-319-48758-8

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-48758-8_24

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