Semiring Programming: A Declarative Framework for Generalized Sum Product Problems
This foundational framework addresses the challenge of integrating diverse AI methods for researchers and practitioners, though it appears incremental as it builds on existing semiring concepts.
The paper tackles the lack of integration among AI disciplines like logic and probabilistic reasoning by introducing a declarative programming framework based on semiring-labeled first-order structures, enabling the combination of problems such as SAT and Bayesian inference.
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, and convex optimization. The semantics of programs is defined in terms of first-order structures with semiring labels, which allows us to freely combine and integrate problems from different AI disciplines.