Presentation on theme: "ICT IGCSE. Understand the use of expert systems in Mineral prospecting Car engine fault diagnosis Medical diagnosis Oil/mineral prospecting."— Presentation transcript:
Understand the use of expert systems in Mineral prospecting Car engine fault diagnosis Medical diagnosis Oil/mineral prospecting Plant/animal identification Strategy games eg Chess
An expert system is computer software that attempts to act like a human expert on a particular subject area. Expert systems are often used to advise non- experts in situations where a human expert is unavailable (for example it may be too expensive to employ a human expert, or it might be a difficult to reach the location).
Diagnosing a person’s illness Diagnostics (car engine faults, circuit board faults etc) Prospecting for oil and minerals Tax and financial calculations Chess games Identification of plants, animals, chemical compounds Road scheduling for delivery vehicles
An expert system is a knowledge-based system which attempts to replace a human 'expert' in a particular field. The system will consist of a large database of knowledge facilities for searching the knowledge database a set of rules for making deductions from the data An engine to apply those rules (inference engine)
An expert system is made up of four parts: A user interface A knowledge base A rules base An inference engine
This is the system that allows a non-expert user to query (question) the expert system, and to receive advice. The user- interface is designed to be a simple to use as possible.
This is a collection of facts. The knowledge base is created from information provided by human experts. It is a database designed to allow the complex storage and retrieval requirements of the expert systems
This is made up of a series of ‘inference rules’ IF the country is in South America AND the language is Portuguese, THEN the country must be Brazil These inference rules are used by the inference engine to draw conclusions. The inference rules closely follow human reasoning.
This acts rather like a search engine, examining the knowledge base for information that matches the user's query. It is software that attempts to derive answers from the knowledge base using a form of reasoning.
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.
Medical diagnosis (the knowledge base would contain medical information, the symptoms of the patient would be used as the query, and the advice would be a diagnose of the patient’s illness)
Playing strategy games like chess against a computer (the knowledge base would contain strategies and moves, the player's moves would be used as the query, and the output would be the computer's 'expert' moves)
Providing financial advice - whether to invest in a business, etc. (the knowledge base would contain data about the performance of financial markets and businesses in the past)
Helping to identify items such as plants / animals / rocks / etc. (the knowledge base would contain characteristics of every item, the details of an unknown item would be used as the query, and the advice would be a likely identification)
Helping to discover locations to drill for water / oil (the knowledge base would contain characteristics of likely rock formations where oil / water could be found, the details of a particular location would be used as the query, and the advice would be the likelihood of finding oil / water there)
Helping to diagnose car engine problems (like medical diagnosis, but for cars!) Two types: Onboard In workshops
Human experts make mistakes all the time (people forget things, etc.) so you might imagine that a computer-based expert system would be much better to have around.
An ES can store far more information than a human. Expert systems provide consistent answers. ES does not 'forget’ to ask important questions, or make mistakes. Reduces the time taken to solve a problem Data can be kept up-to-date.
Data can be collected from many experts The expert system is always available 24 hours a day and will never 'retire'. The system can be used at a distance over a network. A less skilled workforce is needed Opportunity to save money People/areas can access expertise they couldn’t otherwise afford
Very expensive to set up in the first place The computer’s reasoning is only as good as the rules it has been given They have no 'common sense' (a human user tends to notice obvious errors, whereas a computer wouldn't)
Errors in the knowledge base can lead to incorrect decisions being made Mistakes made when entering facts/answers to questions can lead to incorrect decisions Not possible to break ALL expert knowledge into facts, rules & probabilities No human interaction/human touch Considerable training is required to ensure the system is used correctly by the operators