Progress within many areas of Artificial Intelligence, including natural language processing (NLP), vision and expert systems, depends heavily on the proper formalization of common- sense reasoning. A fundamental of this kind of reasoning is its nonmonotonicity, that is its ability to draw conclusions which can be invalidated by new information. In a systematic survey of the leading approaches to the problem of formalizing various nonmonotonic inference patterns, the book carefully blends leading edge research ideas with readability. The author has taken great care to ensure that it is a totally self-contained work, incorporating chapters on automatic theorem proving, main approaches to nonmonotonic reasoning, presenting and analysing several nonmonotonic formalisms: it is addressed to research workers in AI and many related disciplines, as well as practitioners and those involved with expert and intelligent knowledge-based systems. The book will be of benefit to researchers and practitioners interested in artificial intelligence, intelligent systems, expert systems, natural language processing, knowledge engineering and computer vision.