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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]
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.
A key component of the system architecture for all expert systems is the knowledge base, which stores facts and rules for problem-solving. [53] The simplest approach for an expert system knowledge base is simply a collection or network of production rules. Production rules connect symbols in a relationship similar to an If-Then statement.
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.
A classic example of a production rule-based system is the domain-specific expert system that uses rules to make deductions or choices. [1] For example, an expert system might help a doctor choose the correct diagnosis based on a cluster of symptoms, or select tactical moves to play a game.
The Rete algorithm (/ ˈ r iː t iː / REE-tee, / ˈ r eɪ t iː / RAY-tee, rarely / ˈ r iː t / REET, / r ɛ ˈ t eɪ / reh-TAY) is a pattern matching algorithm for implementing rule-based systems. The algorithm was developed to efficiently apply many rules or patterns to many objects, or facts , in a knowledge base .
The fuzzy logic which enables the computer to understand natural language, [2] [page needed] [3] artificial neural networks which permits the system to learn experiential data by operating like the biological one, evolutionary computing, which is based on the process of natural selection, learning theory, and probabilistic methods which helps ...
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.