The original vision of AI was to develop intelligent artifacts with the same broad range of capabilities as we observe in humans. None of the field’s early programs achieved this lofty goal, but the tone of many seminal papers makes this motivation very clear. When we entered graduate school at Carnegie Mellon University in the mid 1970s, this aim was a central tenet of many researchers in the ...

Artificial intelligent tools such as expert systems, artificial neural network and fuzzy logic support decision-making are being used in intelligent manufacturing systems. Success of intelligent manufacturing systems depends on effective and efficient utilization of intelligent tools. Weld residual stress depends on different process parameters and its prediction and control is a challenge to t...

Artificial feelings and emotions are beginning to play an increasingly important role as mechanisms for facilitating learning in intelligent systems. Here we present an architectural framework for artificial neural emotions through the use of an emotional memory system, based on Dr. Peter Levine's Autonomic Nervous System States. Tying the notions of Human Autonomic Nervous System States to an ...

The objective of this special session is to analyze the modus operandi of the intelligent systems from the point of view of the limitations between the machine and the human. In case of creating artificial intelligence like human intelligence, where will be the edge between human being and the intelligent hardware and software with artificial intellect? In this topic we present the following re...

Action selection is a way of characterizing the most basic problem of intelligent systems: what to do next. In artificial intelligence and computational cognitive science, the action selection problem is typically associated with intelligent agents and animats – artificial systems that exhibit complex behaviour in an agent environment. The term is also sometimes used in ethology or animal behav...

This is a review paper which sets the scene by defining some fundamental concept, such as intelligence and intelligent systems, and then discusses current trends in applications of artificial intelligence in engineering. The author reviews five key paradigms of artificial intelligence in engineering: knowledge-based systems, neural networks, genetic algorithms, fuzzy logic and intelligent agents.

Artificial Intelligence (AI) encompasses the following general areas of research: (1) automatic programming, (2) computer vision, (3) expert systems, (4) intelligent computer-assisted instruction, (5) natural language processing, (6) planning and decision support, (7) robotics, and (8) speech recognition. Intelligent library systems utilize artificial intelligence technologies to provide knowle...