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.
- Pub Date:
- September 2016
- Computer Science - Artificial Intelligence;
- Computer Science - Machine Learning;
- Computer Science - Logic in Computer Science
- In AAAI Workshop: Statistical Relational Artificial Intelligence, 2020