Representations of Commonsense Knowledge (Morgan Kaufmann Series in Representation and Reasoning)

A central goal of artificial intelligence is to give a computer program commonsense understanding of basic domains such as time, space, simple laws of nature, and simple facts about human minds. Many different systems of representation and inference have been developed for expressing such knowledge and reasoning with it. "Representations of Commonsense Knowledge" is the first thorough study of these techniques. The first three chapters of the book establish a general framework in domain-independent terms, discussing methodology, deductive logics, and theories of plausible inference. Subsequent chapters each deal with representations and inferences in specific domains: quantities, time, space, physics, knowledge and belief, plans and goals, and interactions among agents. The power of these representations in expressing world knowledge and in supporting significant inferences is analyzed using many detailed examples. The discussion includes both representations that have been used in successful AI programs and those that have been developed in purely abstract settings. Representations of Commonsense Knowledge is an essential reference for AI researchers and developers. It can also be used as a textbook in advanced undergraduate or graduate courses; each chapter contains exercises and suggestions for further reading. Readers who have completed the book will be prepared to read original technical papers in the area and to begin their own work in developing useful representations for AI programs.