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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]
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 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 .
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.
Artificial intelligence (AI), in its broadest sense, is intelligence exhibited by machines, particularly computer systems.It is a field of research in computer science that develops and studies methods and software that enable machines to perceive their environment and use learning and intelligence to take actions that maximize their chances of achieving defined goals. [1]
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 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.
Specifically, each gating is a linear-ReLU-linear-softmax network, and each expert is a linear-ReLU network. Since the output from the gating is not sparse, all expert outputs are needed, and no conditional computation is performed. The key goal when using MoE in deep learning is to reduce computing cost.