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Multiple Approaches to Intelligent Systems [electronic resource] :12th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems IEA/AIE-99, Cairo, Egypt, May 31 - June 3, 1999. Proceedings /

Contributor(s): Imam, Ibrahim [editor.] | Kodratoff, Yves [editor.] | El-Dessouki, Ayman [editor.] | Ali, Moonis [editor.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Lecture Notes in Computer Science: 1611Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 1999.Description: XXXVIII, 904 p. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783540487654.Subject(s): Computer science | Artificial intelligence | Computer simulation | Complexity, Computational | Computer Science | Artificial Intelligence (incl. Robotics) | Simulation and Modeling | ComplexityOnline resources: Click here to access online
Contents:
Invited Talks -- Fuzzy Systems -- Neural Networks -- Genetic Algorithms -- Search -- Reasoning -- Expert Systems and Applications -- Case-Base Reasoning -- Intelligent Agents -- Distributed AI -- Machine Learning -- Temporal Reasoning -- Knowledge Representation -- Planning and Scheduling -- Tutoring and Manufacturing Systems -- Intelligent Software Engineering.
In: Springer eBooksSummary: We never create anything, We discover and reproduce. The Twelfth International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems has a distinguished theme. It is concerned with bridging the gap between the academic and the industrial worlds of Artificial Intelligence (AI) and Expert Systems. The academic world is mainly concerned with discovering new algorithms, approaches, and methodologies; however, the industrial world is mainly driven by profits, and concerned with producing new products or solving customers’ problems. Ten years ago, the artificial intelligence research gap between academia and industry was very broad. Recently, this gap has been narrowed by the emergence of new fields and new joint research strategies in academia. Among the new fields which contributed to the academic-industrial convergence are knowledge representation, machine learning, searching, reasoning, distributed AI, neural networks, data mining, intelligent agents, robotics, pattern recognition, vision, applications of expert systems, and others. It is worth noting that the end results of research in these fields are usually products rather than empirical analyses and theoretical proofs. Applications of such technologies have found great success in many domains including fraud detection, internet service, banking, credit risk and assessment, telecommunication, etc. Progress in these areas has encouraged the leading corporations to institute research funding programs for academic institutes. Others have their own research laboratories, some of which produce state of the art research.
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Invited Talks -- Fuzzy Systems -- Neural Networks -- Genetic Algorithms -- Search -- Reasoning -- Expert Systems and Applications -- Case-Base Reasoning -- Intelligent Agents -- Distributed AI -- Machine Learning -- Temporal Reasoning -- Knowledge Representation -- Planning and Scheduling -- Tutoring and Manufacturing Systems -- Intelligent Software Engineering.

We never create anything, We discover and reproduce. The Twelfth International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems has a distinguished theme. It is concerned with bridging the gap between the academic and the industrial worlds of Artificial Intelligence (AI) and Expert Systems. The academic world is mainly concerned with discovering new algorithms, approaches, and methodologies; however, the industrial world is mainly driven by profits, and concerned with producing new products or solving customers’ problems. Ten years ago, the artificial intelligence research gap between academia and industry was very broad. Recently, this gap has been narrowed by the emergence of new fields and new joint research strategies in academia. Among the new fields which contributed to the academic-industrial convergence are knowledge representation, machine learning, searching, reasoning, distributed AI, neural networks, data mining, intelligent agents, robotics, pattern recognition, vision, applications of expert systems, and others. It is worth noting that the end results of research in these fields are usually products rather than empirical analyses and theoretical proofs. Applications of such technologies have found great success in many domains including fraud detection, internet service, banking, credit risk and assessment, telecommunication, etc. Progress in these areas has encouraged the leading corporations to institute research funding programs for academic institutes. Others have their own research laboratories, some of which produce state of the art research.

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