Intelligent Problem Solving. Methodologies and Approaches 13th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, IEA/AIE 2000 New Orleans, Louisiana, USA, June 19–22, 2000 Proceedings / [electronic resource] :
edited by Rasiah Logananthara, Günther Palm, Moonis Ali.
- Berlin, Heidelberg : Springer Berlin Heidelberg, 2000.
- XVII, 754 p. online resource.
- Lecture Notes in Computer Science, 1821 0302-9743 ; .
- Lecture Notes in Computer Science, 1821 .
Keynote Presentation -- Intelligent Agents I -- Artificial Neural Network I -- Data Mining I -- Combinatorial Optimization -- Expert Systems I -- Diagnosis I -- Best Papers -- Information Systems I -- Fuzzy Logic and Its Applications -- Intelligent Agents II -- Design -- Diagnosis II -- Expert Systems II -- Machine Learning and Its Applications -- Logic and Its Applications -- Pattern Recognition -- Artificial Neural Networks II -- Natural Language Processing -- Genetic Algorithm -- Information Systems II -- Distributed Problem Solving -- Intelligent Agents III -- Artificial Neural Networks III.
The focus of the papers presented in these proceedings is on employing various methodologies and approaches for solving real-life problems. Although the mechanisms that the human brain employs to solve problems are not yet completely known, we do have good insight into the functional processing performed by the human mind. On the basis of the understanding of these natural processes, scientists in the field of applied intelligence have developed multiple types of artificial processes, and have employed them successfully in solving real-life problems. The types of approaches used to solve problems are dependant on both the nature of the problem and the expected outcome. While knowledge-based systems are useful for solving problems in well-understood domains with relatively stable environments, the approach may fail when the domain knowledge is either not very well understood or changing rapidly. The techniques of data discovery through data mining will help to alleviate some problems faced by knowledge-based approaches to solving problems in such domains. Research and development in the area of artificial intelligence are influenced by opportunity, needs, and the availability of resources. The rapid advancement of Internet technology and the trend of increasing bandwidths provide an opportunity and a need for intelligent information processing, thus creating an excellent opportunity for agent-based computations and learning. Over 40% of the papers appearing in the conference proceedings focus on the area of machine learning and intelligent agents - clear evidence of growing interest in this area.
9783540450498
10.1007/3-540-45049-1 doi
Computer science.
Artificial intelligence.
Computer Science.
Artificial Intelligence (incl. Robotics).
Q334-342 TJ210.2-211.495
006.3
Keynote Presentation -- Intelligent Agents I -- Artificial Neural Network I -- Data Mining I -- Combinatorial Optimization -- Expert Systems I -- Diagnosis I -- Best Papers -- Information Systems I -- Fuzzy Logic and Its Applications -- Intelligent Agents II -- Design -- Diagnosis II -- Expert Systems II -- Machine Learning and Its Applications -- Logic and Its Applications -- Pattern Recognition -- Artificial Neural Networks II -- Natural Language Processing -- Genetic Algorithm -- Information Systems II -- Distributed Problem Solving -- Intelligent Agents III -- Artificial Neural Networks III.
The focus of the papers presented in these proceedings is on employing various methodologies and approaches for solving real-life problems. Although the mechanisms that the human brain employs to solve problems are not yet completely known, we do have good insight into the functional processing performed by the human mind. On the basis of the understanding of these natural processes, scientists in the field of applied intelligence have developed multiple types of artificial processes, and have employed them successfully in solving real-life problems. The types of approaches used to solve problems are dependant on both the nature of the problem and the expected outcome. While knowledge-based systems are useful for solving problems in well-understood domains with relatively stable environments, the approach may fail when the domain knowledge is either not very well understood or changing rapidly. The techniques of data discovery through data mining will help to alleviate some problems faced by knowledge-based approaches to solving problems in such domains. Research and development in the area of artificial intelligence are influenced by opportunity, needs, and the availability of resources. The rapid advancement of Internet technology and the trend of increasing bandwidths provide an opportunity and a need for intelligent information processing, thus creating an excellent opportunity for agent-based computations and learning. Over 40% of the papers appearing in the conference proceedings focus on the area of machine learning and intelligent agents - clear evidence of growing interest in this area.
9783540450498
10.1007/3-540-45049-1 doi
Computer science.
Artificial intelligence.
Computer Science.
Artificial Intelligence (incl. Robotics).
Q334-342 TJ210.2-211.495
006.3