DisadvantagesEdit

Characteristics of an Expert System : One expert system may contain knowledge from more than one human experts thus making the solutions more efficient. It decreases the cost of consulting an expert for various domains such as medical diagnosis. They use a knowledge base and inference engine.

A rule-based expert system is the simplest form of artificial intelligence and uses prescribed knowledge-based rules to solve a problem 1. The aim of the expert system is to take knowledge from a human expert and convert this into a number of hardcoded rules to apply to the input data.

• No common sense used in making decisions.
• Lack of creative responses that human experts are capable of.
• Not capable of explaining the logic and reasoning behind a decision.
• It is not easy to automate complex processes.
• There is no flexibility and ability to adapt to changing environments.

An expert system (ES) is a knowledge-based system that employs knowledge about its application domain and uses an inferencing (reason) procedure to solve problems that would otherwise require human competence or expertise.

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 early expert systems such as Mycin and Dendral, these facts were represented mainly as flat assertions about variables.

There are mainly five types of expert systems. They are rule based expert system, frame based expert system, fuzzy expert system, neural expert system and neuro-fuzzy expert system.

The basic objective of AI (also called heuristic programming, machine intelligence, or the simulation of cognitive behavior) is to enable computers to perform such intellectual tasks as decision making, problem solving, perception, understanding human communication (in any language, and translate among them), and the …

Explanation: Expanding is not Capabilities of Expert Systems.

Expert systems are capable of handling enormously complex tasks and activities as well as an extremely rich knowledge-database structure and content. As such, they are well suited to model human activities and problems. Expert systems can reduce production downtime and, as a result, increase output and quality.

An expert system is typically composed of at least three primary components. These are the inference engine, the knowledge base, and the User interface.

An expert system mainly consists of three components:

• User Interface.
• Inference Engine.
• Knowledge Base.

Top 4 Components of Expert System | MIS

• Expert System Component # 1. Knowledge Acquisition Subsystem:
• Expert System Component # 2. Knowledge Base:
• Expert System Component # 3. Interference Engine:
• Expert System Component # 4. User Interface:

An expert system generally consists of four components: a knowledge base, the search or inference system, a knowledge acquisition system, and the user interface or communication system. Knowledge systems solve difficult problems of the real woorld by performing inference processes on explicitly stated knowledge.

Expert systems are special databases which are designed to mimic (copy) the expertise and knowledge of a human expert in various different subjects. Experts are interviewed and their knowledge is gathered and put into a knowledge base. Knowledge from human experts is entered into the system and stored.

The non-expert user queries the expert system. This is done by asking a question, or by answering questions asked by the expert system. The inference engine uses the query to search the knowledge base and then provides an answer or some advice to the user.

Which of the following is incorrect Expert Systems Limitations? Explanation: Easy to maintain is incorrect Expert Systems Limitations.

Which is mainly used for automated reasoning? Explanation: Logic programming is mainly used to check the working process of the system.

Can expert systems make mistakes? Even a brilliant expert is only a human and thus can make mistakes. This suggests that an expert system built to perform at a human expert level also should be allowed to make mistakes. But we still trust experts, even we recognise that their judgements are sometimes wrong.

Advantages of Using Expert System:

• 1] Providing consistent solutions:
• 2] Provides reasonable explanations:
• 3] Overcome human limitations:
• 4] Easy to adapt to new conditions:
• 1] Lacks common sense:
• 2] High implementation and maintenance cost:
• 3] Difficulty in creating inference rules:
• 4] May provide wrong solutions:

The purpose of medical expert system is to support the diagnosis process of physicians. It considers facts and symptoms to provide diagnosis. This implies that a medical expert system uses knowledge about diseases and facts about the patients to suggest diagnosis.

The user interface is the most crucial part of the Expert System Software. This component takes the user’s query in a readable form and passes it to the inference engine. After that, it displays the results to the user. In other words, it’s an interface that helps the user communicate with the expert system.

ohn McCarthy

An expert system is a computer program that uses artificial intelligence (AI) technologies to simulate the judgment and behavior of a human or an organization that has expert knowledge and experience in a particular field.

Here is a six-step formula for building your core expert systems.

1. Step One: Define All Deliverables.
2. Step Two: Lay Out the Process.
3. Step Three: Determine the Optimal Level of Expertise for Each Step.
4. Step Four: Control for Consistency.
5. Step Five: Map Out the Key Components of Your Expert System to Refine First.

Diagnostic expert-based systems are computer systems that seek to emulate the diagnostic decision-making ability of human experts. Some notable systems include Mycin for infectious diseases, and Internist-1, QMR and DXplain for general internal medicine.

Applications of Expert System

Question 1 What is the main purpose of Expert Systems? Answer: The main purpose of ES is to replicate knowledge and skills of human experts in a particular area, and then to use this knowledge to solve similar problems without human experts participation (computationally).

The internal structure of an expert system can be considered to consist of three parts: the knowledge base ; the database; the rule interpreter. the set of productions; the set of facts held as working memory and a rule interpreter. The knowledge base holds the set of rules of inference that are used in reasoning.

Waterman [11] provided five phases/stages approach in the development of Expert System: Identification , Conceptualization, Formalization, Implementation, and Testing.

The forms of knowledge representation typically used in expert systems are: structured objects (frames, semantic networks, object-oriented principles), rules (if-then) and logic (predicate, proposi- tional). Reasoning strength: it must be possible by reasoning to deduce new knowledge from basic knowledge.