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
actions additional algorithm allows apply arguments assembled assigned associated assume axioms believe block called chapter CLEAR concept conjunction consider consistent corresponding database decision defined defun dependency described determine distribution edge environment equal evaluation evidence example existing expression fact False Figure formula function given goal holds implementation indicates inference initial input instance interpretation involving knowledge labeled lambda language learning light Lisp logic match method move node objects operator output particular perform possible probability problem projection propositional prove quantified reasoning reports represent representation result returns robot rule scene search space shown shows simple solution space specified step structure Suppose surface symbol takes techniques theory tree True variables weights