Non Monotonic

Non-monotonic reasoning focuses on developing computational models that can handle incomplete or contradictory information, mirroring human reasoning's ability to revise conclusions in light of new evidence. Current research emphasizes the development and application of formalisms like Answer Set Programming (ASP) and neural-symbolic systems, often coupled with large language models, to address challenges in areas such as argumentation, planning, and visual reasoning. These advancements are improving the ability of AI systems to handle real-world complexities and uncertainty, with applications ranging from robotic task planning to more human-like problem-solving in diverse domains.