The paper by R. Reiter introduces a logic for default reasoning, which is essential in Artificial Intelligence for making inferences in incomplete or uncertain environments. The author defines default rules as a way to make assumptions that can be modified or rejected based on new information. These rules are non-monotonic, meaning that adding new information can change the set of beliefs or conclusions. The paper discusses various examples of default reasoning, such as the closed world assumption, frame defaults, and exceptions, and provides a formal framework for understanding and implementing these rules. It also explores the concept of extensions for default theories, which are sets of beliefs that can be derived from a set of defaults and initial knowledge. The paper concludes with a proof theory for normal defaults, which allows for local reasoning and the revision of beliefs when new information is encountered.The paper by R. Reiter introduces a logic for default reasoning, which is essential in Artificial Intelligence for making inferences in incomplete or uncertain environments. The author defines default rules as a way to make assumptions that can be modified or rejected based on new information. These rules are non-monotonic, meaning that adding new information can change the set of beliefs or conclusions. The paper discusses various examples of default reasoning, such as the closed world assumption, frame defaults, and exceptions, and provides a formal framework for understanding and implementing these rules. It also explores the concept of extensions for default theories, which are sets of beliefs that can be derived from a set of defaults and initial knowledge. The paper concludes with a proof theory for normal defaults, which allows for local reasoning and the revision of beliefs when new information is encountered.