Generating Random Logic Programs Using Constraint Programming

Paulius Dilkas, Vaishak Belle

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

Testing algorithms across a wide range of problem instances is crucial to ensure the validity of any claim about one algorithm's superiority over another. However, when it comes to inference algorithms for probabilistic logic programs, experimental evaluations are limited to only a few programs. Existing methods to generate random logic programs are limited to propositional programs and often impose stringent syntactic restrictions.We present a novel approach to generating random logic programs and random probabilistic logic programs using constraint programming, introducing a new constraint to control the independence structure of the underlying probability distribution. We also provide a combinatorial argument for the correctness of the model, show how the model scales with parameter values, and use the model to compare probabilistic inference algorithms across a range of synthetic problems. Our model allows inference algorithm developers to evaluate and compare the algorithms across a wide range of instances, providing a detailed picture of their (comparative) strengths and weaknesses.
Original languageEnglish
Title of host publicationPrinciples and Practice of Constraint Programming
Subtitle of host publication26th International Conference, CP 2020, Louvain-la-Neuve, Belgium, September 7–11, 2020, Proceedings
Number of pages18
ISBN (Electronic)978-3-030-58475-7
ISBN (Print)978-3-030-58474-0
Publication statusPublished - 2 Sept 2020
Event26th International Conference on Principles and Practice of Constraint Programming - Louvain-la-Neuve, Belgium
Duration: 7 Sept 202011 Sept 2020

Publication series

Name Lecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference26th International Conference on Principles and Practice of Constraint Programming
Abbreviated titleCP 2020
Internet address

Keywords / Materials (for Non-textual outputs)

  • Constraint programming
  • Probabilistic logic programming
  • Statistical relational learning


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