Intelligent Systems for Engineering: A Knowledge-based ApproachWhen 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. |
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Intelligent Systems for Engineering: A Knowledge-based Approach Ram D. Sriram No preview available - 2012 |
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action ACTIVE stock analysis application approach Artificial Intelligence attributes backward chaining behavior blackboard system breadth-first search candidate case-based reasoning causal Chapter components Computer configuration space constraints data panel defined depth-first search described developed device diagnosis dim or dead domain Editor ellipsoidal engineering equations evaluation example Expert Systems floor-sys floor-sys-1 forward chaining frame function genetic algorithms goal graph heuristics hierarchy hypothesis implemented inference mechanism initial input interface is-a KBES knowledge representation knowledge sources knowledge-base learning levels of abstraction match messages model-based modified neural network nodes object object-oriented object-oriented programming operators output parameters perform problem solving procedure qualitative reasoning relations representation represented Request panel retrieved rules search methods Section selected semantic networks shown in Figure simulated annealing slot solution solver specific starting system Strategy KS structure subgoals task techniques test vectors tion variables various