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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Boyer and Moore (1979) have developed a theorem-proving system that proves theorems about recursive functions and makes strong use of induction.
A simple recursive procedure captures the essence of the operation of a production system under backtracking control. This procedure, which we call ...
8 PATH – BACKTRACK(RDATA); BACKTRACK is called recursively on the new database. 9 if PATH = FAIL, go LOOP; if the recursive call fails, try another rule.
Any recursive call fails when its depth exceeds this bound. Cycling can be more straightforwardly prevented by maintaining a list of the databases produced ...
In order to implement this backtracking strategy as a recursive procedure, the entire chain of databases must be an argument of the procedure.
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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