Intelligent systems engineering (ISE) refers to various AI approaches for industrial challenges. ISE programs exist in mechanical engineering departments and are designed to be adaptive and solve problems creatively. They follow a sequence of events to diagnose and address problems and seek to create networks of sensors that act as virtual observers.
Intelligent systems engineering (ISE) is an umbrella term used to refer to a variety of artificial intelligence (AI) approaches, including neural networks, evolutionary algorithms, model-based prediction and control, case-based diagnostic systems, control theory conventional and AI symbols. The term intelligent systems engineering is most frequently used in the context of artificial intelligence applied to specific industrial challenges such as optimizing a process sequence in a sugar mill. This type of engineering tends to refer to creating short-term, narrow-tasking, marketable AI, rather than long-term, flexible, and generally intelligent AI.
There are university departments in a number of countries that focus on intelligent systems engineering. Both the terminology and general philosophy of ISE are derived from a blend of mechanical and electrical engineering and computer science. ISE programs often exist within mechanical engineering departments.
Intelligent systems are generally meant to be coupled with robotics in industrial process settings, although they can be diagnostic systems linked only to passive sensors. Intelligent systems are designed to be adaptive, to solve problems as creatively as possible with minimal human input. The field has received significant investment from both the private sector and the military.
Intelligent systems generally follow a sequence of events in diagnosing and addressing a potential problem. First, the system identifies and defines the problem. It then identifies evaluation criteria to apply to the situation, which it uses to generate a set of alternatives to the problem.
There is an iterative search for a solution and an evaluation of potential solutions, until a choice and recommendation is made. So sometimes with the required human go-ahead, the fix is implemented. Intelligent systems relieve some of human stress by automatically solving the simplest of the many thousands of problems that arise in industrial process settings.
Intelligent systems engineering seeks to create networks of sensors that not only take numerical readings but also act as virtual observers, integrating sense data and making generalizations. As our technology infrastructure becomes increasingly complex, many workers are welcoming artificial assistance in diagnosing and resolving problems.
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