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Presentation 5: Correlation of Artificial Intelligence Techniques with Soft Computing

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Published on Nov 16, 2016

Author's: Avinash Kumar, Department of Mechanical Engg. Government Engg. Ramgarh Jharkhand, India- 825101
Abhishek Kumar, Department of ECE BIT Mesra, Ranchi Jharkhand, India-835215.
Arun Prasad Burnwal, Department of Mathematics GGSESTC, Bokaro Jharkhand, India-827013.
Email: avikr1208@gmail.com
Artificial intelligence (AI) is a part of computer science concerned with designing intelligent computer systems that exhibit the characteristics used to associate with intelligence in human behavior. Basically it define as a field that study and design of intelligent agents. This agent is a method that perceives its environment and takes actions that maximize its chances of success and reduce the complexity of system. AI tries to solve the complex problems in more human like fashion and in relatively very less time than a human takes. Traditional AI approach deals with cognitive and biological models that imitate and describe human information processing skills. This processing skills help to perceive and interact with their environment. But in modern era developers can build systems that assemble superior information processing needs of government and industry by choosing from large areas of mature technologies. Complicated techniques for reasoning about uncertainty are coping into incomplete knowledge to make strong analytical and planning systems. The core elements of AI are reasoning, knowledge, planning, learning, natural language processing, perception and the ability to move and manipulate entities. These core elements help to deals with various kinds of knowledge representation schemes, different techniques of intelligent search, various methods for resolving uncertainty of data and knowledge and many others. The parent disciplines of AI are philosophy, cognitive science, mathematics, and psychology. Soft computing (SC) is a newly added area of AI. It focused on the design of intelligent systems to process uncertain, imprecise and incomplete information. SC methods applied to real world problems frequently offer more robust, tractable and less costly solutions than those obtained by more conventional mathematical techniques. Chaotic computing and immune systems are added later to enhance the SC capabilities. The correlation of AI techniques with SC components are increasing rapidly in numerous areas such as agriculture, civil engineering, computer engineering, industrial automation and manufacturing, management and finance, medical computing, robotics, short term load forecasting, transportation, water resource management, material science, fault diagnosis, signal processing etc. This paper reviews the use of correlation of artificial intelligence techniques with soft computing in various areas.

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