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 Intelligence"evolved 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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can be answered if we first prove that the wff ( Ex ) AT ( FIDO , x ) logically follows
from S and then find an instance of the ... containing an existential quantifier such
that the existentially quantified variable represents an answer to the question .
We note that the answer statement has a form similar to that of the goal wff . In
this case , the only difference is that we have a constant ( the answer ) in the
answer statement in the place of the existentially quantified variable in the goal
In conclusion , the steps of the answer extraction process can be summarized as
follows : 1 . A resolution - refutation tree is found by some search process . The
unification subsets of the clauses in this tree are marked . 2 . New variables are ...
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