Principles of Artificial Intelligence
A classic introduction to artificial intelligence intended to bridge the gap between theory and practice, Principles of Artificial Intelligence describes fundamental AI ideas that underlie applications such as natural language processing, automatic programming, robotics, machine vision, automatic theorem proving, and intelligent data retrieval. Rather than focusing on the subject matter of the applications, the book is organized around general computational concepts involving the kinds of data structures used, the types of operations performed on the data structures, and the properties of the control strategies used.
Principles of Artificial Intelligenceevolved from the author's courses and seminars at Stanford University and University of Massachusetts, Amherst, and is suitable for text use in a senior or graduate AI course, or for individual study.
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... called Artificial Intelligence (AI). This work has had largely an empirical and engineering orientation. Drawing from a loosely structured but growing body of computational techniques, AI systems are developed, undergo experimentation ...
... called program verification. Many automatic programming systems produce a verification of the output program as an added benefit. One of the important contributions of research in automatic programming has been the notion of debugging ...
... have suggested that this knowledge be organized in special structures called frames or schemas. For example, when a robot enters a room through a doorway, it activates a room schema, which loads into working 8 PROLOGUE.
... called AND/OR graphs, and to the graphs that arise in analyzing certain games. In chapter 4, we introduce the predicate calculus and describe the important role that it plays in AI systems. Various rules of inference, including ...
... called frame-based representations. Our point of view toward such representations is that they can best be understood as a form of predicate calculus. Last, in the prospectus, we review some outstanding AI problems that are not yet ...
CHAPTER 3 SEARCH STRATEGIES FOR DECOMPOSABLE PRODUCTION SYSTEMS
CHAPTER 4 THE PREDICATE CALCULUS IN AI
CHAPTER 5 RESOLUTION REFUTATION SYSTEMS
CHAPTER 6 RULEBASED DEDUCTION SYSTEMS
CHAPTER 7 BASIC PLANGENERATING SYSTEMS
CHAPTER 8 ADVANCED PLANGENERATING SYSTEMS
CHAPTER 9 STRUCTURED OBJECT REPRESENTATIONS