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An expert system is an example of a knowledge-based system. Expert systems were the first commercial systems to use a knowledge-based architecture. In general view, an expert system includes the following components: a knowledge base, an inference engine, an explanation facility, a knowledge acquisition facility, and a user interface. [48] [49]
Akshay Phadké has spent the past decade in tech, from Big Tech to startups. He faced countless rejections before four résumé strategies helped him land a job at Microsoft.
In the field of artificial intelligence, an inference engine is a software component of an intelligent system that applies logical rules to the knowledge base to deduce new information. The first inference engines were components of expert systems. The typical expert system consisted of a knowledge base and an inference engine.
The task for the programmer is to find an algorithm which is able to control the robot, so that it can do a task. In the history of robotics and optimal control [1] there were many paradigm developed. One of them are expert systems, which is focused on restricted domains. [2] Expert systems are the precursor to model based systems.
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In the earliest days of expert systems, there was little or no formal process for the creation of the software. Researchers just sat down with domain experts and started programming, often developing the required tools (e.g. inference engines ) at the same time as the applications themselves.
The first knowledge-based systems were primarily rule-based expert systems. These represented facts about the world as simple assertions in a flat database and used domain-specific rules to reason about these assertions, and then to add to them. One of the most famous of these early systems was Mycin, a program for medical diagnosis.