Knowledge Representation and ReasoningKnowledge 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.
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Contents
| 1 | |
| 15 | |
| 31 | |
Chapter 4 Resolution | 49 |
Chapter 5 Reasoning with Horn Clauses | 85 |
Chapter 6 Procedural Control of Reasoning | 99 |
Chapter 7 Rules in Production Systems | 117 |
Chapter 8 ObjectOriented Representation | 135 |
Chapter 11 Defaults | 205 |
Chapter 12 Vagueness Uncertainty and Degrees of Belief | 237 |
Chapter 13 Explanation and Diagnosis | 267 |
Chapter 14 Actions | 285 |
Chapter 15 Planning | 305 |
Chapter 16 The Tradeoff between Expressiveness and Tractability | 327 |
| 349 | |
| 377 | |
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Common terms and phrases
abductive answer applicable arity Artificial Intelligence assume assumption atomic concepts atomic sentences autoepistemic logic belief network calculate called Chapter circumscription complete computational conclusions consider constants database deductive reasoning default logic default reasoning defined definition degree of belief derivation description logic disjunction domain edge effect axioms empty clause entailed example express extension fact Figure filler first-order logic Flies(tweety fluent formula given goal Horn clauses individual input interpretation John knowledge base knowledge representation knowledge-based literals negation negation as failure negative node normal objects planning Pr(a predicates prime implicates problem procedure production system PROLOG properties propositional query represent representation and reasoning representation language Resolution robot role rule satisfies semantics set of clauses situation calculus slot specific stable expansion subset subsumed subsumption successor state axiom Suppose taxonomy tion TravelStep true Tweety variables