Semiring Programming: A Declarative Framework for Generalized Sum Product Problems

Vaishak Belle, Luc De Raedt

Research output: Contribution to conferencePaperpeer-review

Abstract / Description of output

To solve hard problems, AI relies on a variety of disciplines such as logic, probabilistic reasoning, machine learning and mathematical programming. Although it is widely accepted that solving real-world problems requires an integration amongst these, contemporary representation methodologies offer little support for this.
In an attempt to alleviate this situation, we introduce a new declarative programming framework that provides abstractions of well-known problems such as SAT, Bayesian inference, generative models, learning and convex optimization. The semantics of programs is defined in terms of first-order logic structures with semiring labels, which allows us to freely combine and integrate problems from different AI disciplines and represent non-standard problems over unbounded domains.
Original languageEnglish
Number of pages19
Publication statusPublished - 7 Feb 2020
EventNinth International Workshop on Statistical Relational AI - New York, United States
Duration: 7 Feb 20207 Feb 2020
Conference number: 9


WorkshopNinth International Workshop on Statistical Relational AI
Abbreviated titleStarAI 2020
Country/TerritoryUnited States
CityNew York
Internet address

Keywords / Materials (for Non-textual outputs)

  • Weighted model counting
  • Declarative Languages
  • Semantic abstractions
  • Semiring frameworks


Dive into the research topics of 'Semiring Programming: A Declarative Framework for Generalized Sum Product Problems'. Together they form a unique fingerprint.

Cite this