Artificial Intelligence: Theory and PracticeThis book provides a detailed understanding of the broad issues in artificial intelligence and a survey of current AI technology. The author delivers broad coverage of innovative representational techniques, including neural networks, image processing and probabilistic reasoning, alongside the traditional methods of symbolic reasoning. The work is intended for students in artificial intelligence, researchers and LISP programmers. |
Common terms and phrases
abstract abstract data type algorithm apply assembled assigned axioms basis functions best-first search boolean boolean functions breadth-first search calculus called causal chapter Common Lisp concept conjunction consider consistent constraints corresponding database decision tree defined defun depth-first depth-first search described determine disjunction distribution edge environment expression False formula Fred given goal gradient graph hypothesis space implementation initial input instance interpretation involving labeled learning Lisp logic mapcar lambda match method node objects operator optical flow output parameters particular perform postconditions Pr(A preconditions predicate probabilistic network probability problem propagate propositional quantified random variables reasoning recursive represent representation result robot rules of inference scene schema search space semantics sequence setq shown in Figure situation calculus Sonya specified step structure Suppose surface symbol techniques theorem theory tion training examples True weights