Nonmonotonic Semantic Networks

Nonmonotonic semantic networks are models used to represent knowledge where relationships between concepts can change with new information. Unlike traditional networks that assume facts are always true, nonmonotonic networks allow for default assumptions that can be revised when evidence contradicts them. This flexibility helps simulate real-world reasoning, where conclusions are tentative and updated as new data emerges. In essence, they enable systems to handle uncertainty and incomplete information, making reasoning more adaptable and closer to human thought processes.