Knowledge Representation and Reasoning

Abstract

aspects run the risk of not scaling up properly to account for human level competence.In the end, our view is that Knowledge Representation is the study of how what we know can at the same time be represented as comprehensibly as possible and reasoned with as effectively as possibly.There is a tradeoff between these two concerns, which is an implicit theme throughout the book, and explicit in the final chapter.Although we start with full first-order logic as a representation language, and logical entailment as the basis for reasoning, this is just the starting point, and a somewhat unrealistic one at that.Subsequent chapters expand and enhance the picture by looking at languages with very different intuitions and emphases, and approaches to reasoning sometimes quite removed from logical entailment.Our approach is to explain the key concepts underlying a wide variety of formalisms, without trying to account for the quirks of particular representation schemes proposed in the literature.By exposing the heart of each style of representation, complemented by a discussion of the basics of reasoning with that representation, we aim to give the reader a solid foundation for understanding the more detailed and sophisticated work found in the research literature.The book is organized as follows.The first chapter provides an overview and motivation for the whole area.Chapters 2 through 5 are concerned with the basic techniques of Knowledge Representation using first-order logic in a direct way.These early chapters introduce the notation of first-order logic, show how it can be used to represent commonsense worlds, and cover the key reasoning technique of Resolution theorem-proving.Chapters 6 and 7 are concerned with representing knowledge in a more limited way, so that the reasoning is more amenable to procedural control; among the important concepts covered there we find rule-based production systems.Chapters 8 through 10 deal with a more object-oriented approach to Knowledge Representation and the taxonomic reasoning that goes with it.Here we delve into the ideas of frame representations and description logics, as well as spending time on the notion of inheritance.Chapters 11 and 12 deal with reasoning that is uncertain or not logically guaranteed to be correct, including default reasoning and probabilities.Chapters 13 through 15 deal with forms of reasoning that are not concerned with deriving new beliefs from old ones, including the notion of planning, which is central to AI.Finally, Chapter 16 explores the tradeoff mentioned above.A course based on the topics of this book has been taught a number of times at the University of Toronto.The course comprises about 24 hours of lectures and occasional tutorials, and is intended for upper-level undergraduate students or entrylevel graduate students in Computer Science or a related discipline.Students are expected to have already taken an introductory course in AI where the larger picture 2003