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 language | English |
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Title of host publication | Principles and Practice of Constraint Programming |
Subtitle of host publication | 26th International Conference, CP 2020, Louvain-la-Neuve, Belgium, September 7–11, 2020, Proceedings |
Publisher | Springer |
Pages | 828–845 |
Number of pages | 18 |
ISBN (Electronic) | 978-3-030-58475-7 |
ISBN (Print) | 978-3-030-58474-0 |
DOIs | |
Publication status | Published - 2 Sept 2020 |
Event | 26th International Conference on Principles and Practice of Constraint Programming - Louvain-la-Neuve, Belgium Duration: 7 Sept 2020 → 11 Sept 2020 https://cp2020.a4cp.org/ |
Publication series
Name | Lecture Notes in Computer Science |
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Publisher | Springer |
Volume | 12333 |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | 26th International Conference on Principles and Practice of Constraint Programming |
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Abbreviated title | CP 2020 |
Country/Territory | Belgium |
City | Louvain-la-Neuve |
Period | 7/09/20 → 11/09/20 |
Internet address |
Keywords / Materials (for Non-textual outputs)
- Constraint programming
- Probabilistic logic programming
- Statistical relational learning