Intelligent Systems for Engineering: A Knowledge-based Approach
When men of knowledge impart this knowledge, I do not mean they will convince your reason. I mean they will awaken in you the faith that it is so. - Sri Krishna, Bhagavadgita BACKGROUND The use of computers has led to significant productivity increases in the en gineering industry. Most ofthe computer-aided engineering applications were . restricted to algorithmic computations, such as finite element programs and circuit analysis programs. However, a number ofproblems encountered in en gineering are not amenable to purely algorithmic solutions. These problems are often ill-structured; the term ill-structured problems is used here to de note problems that do not have a clearly defined algorithmic solution. An experienced engineer deals with these ill-structured problems using his/her judgment and experience. The knowledge-based systems (KBS) technology, which emerged out of research in artificial intelligence (AI), offers a method ologyto solve these ill-structuredengineering problems. The emergenceofthe KBS technology can be viewed as the knowledge revolution: other important events that led to increased productivity are the industrial revolution (17th century); the invention of the transistor and associated developments (first half of the 20th century); and the world-wide web (towards the end of the 20th century). Kurzweil, in a lecture at M. LT on December 3, 1987, linked the progress of automation to two industrial revolutions: the first industrial PREFACE xxxii revolution leveraged our physical capabilities, whereas the second industrial revolution - the knowledge revolution - is expected leverage oUr mental ca pabilities.
61 pages matching strategy in this book
Results 1-3 of 61
What people are saying - Write a review
We haven't found any reviews in the usual places.
List of Figures
List of Tables
49 other sections not shown
abstraction action ACTIVE stock analysis application approach Artificial Intelligence associated backward chaining behavior blackboard blackboard system breadth-first search case-based case-based reasoning causal Chapter clothes pin components Computer configuration space connected constraints defined depicted depth-first search described developed device discussed domain Editor ellipsoidal equations evaluation example Expert Systems floor-sys-1 FONM forward chaining frame function genetic algorithms goal headlights heuristics hierarchy hypothesis IEEE implemented inference mechanism initial input instantiate involves KBES knowledge representation knowledge-base language learning logic machine match neural network nodes object object-oriented object-oriented programming operators optimal output panel parameters pattern primitive problem solving procedure programming qualitative reasoning region relations relationships representation represented rule-based rules search methods Section selected semantic networks shown in Figure simulated annealing slab slot solution solver specific starting system strategy structure subgoals task techniques tion variables vector