Defeasible Reasoning

Defeasible reasoning addresses the challenge of drawing conclusions from incomplete or contradictory information, focusing on how to manage exceptions to general rules and retract conclusions when new evidence arises. Current research explores various formalisms, including extensions of KLM logic and the integration of defeasible reasoning with knowledge graphs, argumentation frameworks, and machine learning models like LLMs, often employing techniques like rational closure and beam search for efficient inference. This field is crucial for developing robust AI systems capable of handling real-world uncertainty and ambiguity, with applications ranging from fake news detection to legal reasoning and ethical decision-making.