Short summary

Implementing causal, versus strictly correlative, inference can lead to a deeper understanding of many complex problems. Probabilistic Logic Networks (PLN), with their robust mathematical foundations for handling uncertain and incomplete knowledge—such as induction, abduction, analogy, and reasoning about time—offer a strong framework for this task. This project has two main goals:

1. To guide PLN inference control using causal networks.
2. To explore how PLN rules can distinguish causal from correlational relationships.

Proposals can address either of these goals separately or explore both together and the feedback between them. Submissions should include testable examples to validate the approach and demonstrate how causal reasoning improves PLN’s overall performance.

Main purpose

• To explore how causal learning and causal networks can help guide PLN inference control. 

• To advance research in causal reasoning using PLN, which is foundational for AGI, and thus improve how AI systems distinguish between causality and correlation, leading to more accurate predictions and better decision-making processes.