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.
Results 1-3 of 14
... universally quantified . Furthermore , the order of universal quantification is unimportant , so we may eliminate the explicit occur- rence of universal quantifiers and assume , by convention , that all variables in the matrix are ...
... universally quantified variables . These universally quantified variables become existentially quantified in the negation of the goal wff , causing Skolem functions to be introduced . What is to be the interpretation of these Skolem ...
... universal quantification . We assume that any existential variables in facts and rules have been Skolemized . For goal wffs containing existentially or universally quantified vari- ables , we use a Skolemization process that is dual to ...
PRODUCTION Systems and AI
SEARCH Strategies FOR
10 other sections not shown