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Knowledge Representation and Reasoning von Brachman, Ronald (eBook)

  • Verlag: Elsevier Textbooks
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Knowledge Representation and Reasoning

Knowledge representation is at the very core of a radical idea for understanding intelligence. Instead of trying to understand or build brains from the bottom up, its goal is to understand and build intelligent behavior from the top down, putting the focus on what an agent needs to know in order to behave intelligently, how this knowledge can be represented symbolically, and how automated reasoning procedures can make this knowledge available as needed.

This landmark text takes the central concepts of knowledge representation developed over the last 50 years and illustrates them in a lucid and compelling way. Each of the various styles of representation is presented in a simple and intuitive form, and the basics of reasoning with that representation are explained in detail. This approach gives readers a solid foundation for understanding the more advanced work found in the research literature. The presentation is clear enough to be accessible to a broad audience, including researchers and practitioners in database management, information retrieval, and object-oriented systems as well as artificial intelligence. This book provides the foundation in knowledge representation and reasoning that every AI practitioner needs.

Authors are well-recognized experts in the field who have applied the techniques to real-world problems
Presents the core ideas of KR&R in a simple straight forward approach, independent of the quirks of research systems
Offers the first true synthesis of the field in over a decade

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Knowledge Representation and Reasoning

CHAPTER 1 I NTRODUCTION

Intelligence, as exhibited by people anyway, is surely one of the most complex and mysterious phenomena that we are aware of. One striking aspect of intelligent behavior is that it is clearly conditioned by knowledge: for a very wide range of activities, we make decisions about what to do based on what we know (or believe) about the world, effortlessly and unconsciously. Using what we know in this way is so commonplace that we only really pay attention to it when it is not there. When we say that someone has behaved unintelligently , like when someone has used a lit match to see if there is any gas in a car's gas tank, what we usually mean is not that there is something that the person did not know, but rather that the person has failed to use what he or she did know. We might say, "You weren't thinking!" Indeed, it is thinking that is supposed to bring what is relevant in what we know to bear on what we are trying to do.

One definition of Artificial Intelligence (AI) is that it is the study of intelligent behavior achieved through computational means. Knowledge representation and reasoning, then, is that part of AI that is concerned with how an agent uses what it knows in deciding what to do. It is the study of thinking as a computational process. This book is an introduction to that field and in particular, to the symbolic structures it has invented for representing knowledge and to the computational processes it has devised for reasoning with those symbolic structures.

If this book is an introduction to the area, then this chapter is an introduction to the introduction. In it, we will try to address, if only briefly, some significant questions that surround the deep and challenging topics of the field: What exactly do we mean by "knowledge," by "representation," and by "reasoning," and why do we think these concepts are useful for building AI systems? In the end, these are philosophical questions, and thorny ones at that; they bear considerable investigation by those with a more philosophical bent and can be the subject matter of whole careers. But the purpose of this chapter is not to cover in any detail what philosophers, logicians, and computer scientists have said about knowledge over the years; it is rather to glance at some of the main issues involved, and examine their bearings on Artificial Intelligence and the prospect of a machine that could think.
1.1 THE KEY CONCEPTS: KNOWLEDGE, REPRESENTATION, AND REASONING

Knowledge What is knowledge? This is a question that has been discussed by philosophers since the ancient Greeks, and it is still not totally demystified. We certainly will not attempt to be done with it here. But to get a rough sense of what knowledge is supposed to be, it is useful to look at how we talk about it informally.

First, observe that when we say something like "John knows that ...," we fill in the blank with a simple declarative sentence. So we might say, "John knows that Mary will come to the party," or "John knows that Abraham Lincoln was assassinated." This suggests that, among other things, knowledge is a relation between a knower, like John, and a proposition , that is, the idea expressed by a simple declarative sentence, like "Mary will come to the party."

Part of the mystery surrounding knowledge is due to the nature of propositions. What can we say about them? As far as we are concerned, what matters about propositions is that they are abstract entities that can be true or false, right or wrong. 1 When we say, "John knows that p ," we can just as well say, "John knows that it is true that p. " Either way, to say that John knows something is to say that John has formed a judgment of some sort, and has come to realize that the world is one way and not another. In talking about this judgment,

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