NS-CEP

NS-CEP studies AI systems that combine learning from data with explicit rules so they can work even when information is uncertain. The goal is to extract useful signals and make forecasts about complex events, including events that were not already seen in past data.

This is especially useful in dynamic environments where evidence is noisy, incomplete, or changing quickly. In simple terms, the project tries to build systems that do more than recognise known patterns: they should also reason when the context becomes confusing.

Source: Federico Cerutti projects page.