Chapter 1 - Automating Common Sense

Abstract

This chapter presents a general feeling for the field of AI commonsense reasoning and discusses some basic ideas and terminology. It discusses the kinds of issues that a theory of commonsense reasoning must address or avoid; the general structure of these kinds of theories; the most important methodological debates in the field; and the relationship between theories of common-sense reasoning and other fields of study. For an intelligent creature to act sensibly in the real world, it must know about that world and be able to use its knowledge effectively. Commonsense knowledge and commonsense reasoning are involved in most types of intelligent activities, such as using natural language, planning, learning, high-level vision, and expert-level reasoning. Common sense involves many subtle modes of reasoning and a vast body of knowledge with complex interactions. As common sense consists (by definition) of knowledge and reasoning methods that are utterly obvious, its astonishing scope and power are often overlooked. Commonsense reasoning in AI programs can be viewed as largely the performance of inference on a body of object-level information. A knowledge-based system is a program consisting of two parts: the knowledge base and the knowledge base manager. The chapter discusses the development of declarative representations for the knowledge used in commonsense inferences.