Probabilistic Logic

Probabilistic logic integrates probability theory with logic to represent and reason under uncertainty, aiming to build more robust and explainable AI systems. Current research emphasizes efficient learning of probabilistic logical models, often employing techniques like answer set programming, knowledge compilation, and sampling-based methods within frameworks such as neuro-symbolic systems and probabilistic logic programming languages. These advancements are improving the scalability and accuracy of probabilistic reasoning, with applications ranging from knowledge graph refinement and multi-agent reinforcement learning to more interpretable machine learning models. The development of unified languages and tools for probabilistic logic programming is also a key focus, facilitating broader adoption and comparison of different approaches.