Non-monotonic logic is a type of reasoning where the introduction of new information can change previous conclusions. Unlike traditional logic, where once something is proven true it remains true, non-monotonic logic allows for flexibility and adaptation in reasoning. This is particularly useful in real-world scenarios where information is often incomplete or evolving. In non-monotonic logic, conclusions can be retracted when new evidence contradicts them. This approach is commonly applied in fields like artificial intelligence and knowledge representation, where systems must adjust their beliefs based on new data. It helps create more robust models that can handle uncertainty and change.