Semiring Programming: A Declarative Framework for Generalized Sum Product Problems
Abstract
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 realworld 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 wellknown problems such as SAT, Bayesian inference, generative models, and convex optimization. The semantics of programs is defined in terms of firstorder structures with semiring labels, which allows us to freely combine and integrate problems from different AI disciplines.
 Publication:

arXiv eprints
 Pub Date:
 September 2016
 arXiv:
 arXiv:1609.06954
 Bibcode:
 2016arXiv160906954B
 Keywords:

 Computer Science  Artificial Intelligence;
 Computer Science  Machine Learning;
 Computer Science  Logic in Computer Science
 EPrint:
 In AAAI Workshop: Statistical Relational Artificial Intelligence, 2020