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Introduction

Define what is meant by the term Artificial Intelligence Artificial intelligence is a branch of computer science that deals with the creation of computer programs that can provide solutions that otherwise humans would have to solve. On a broader spectrum artificial intelligence attempts to imitate human behavior and intelligence to generate these computer programs. However artificial intelligence is the youngest of studies, and is evolving every day. The ultimate aim of artificial intelligence and its study is to imitate and/or duplicate intelligence of humans in computers and robots. Artificial Intelligence improves productivity, personnel upgrading, new training and to aid in the solving of difficult problems. With the aid of a suitable diagram, illustrate the branches of A.I and define where Expert Systems reside within it. [http://distancelearning.ksi.edu/demo/509/ch01a.html] The diagram above illustrates the seven (7) areas that Artificial Intelligence is comprised of. Of course with the speed of progression of such a complex study such as Artificial Intelligence these areas are rapidly growing however they are more commonly defined in to one of these seven 'branches'. "AI has many areas of interest. The area of expert systems is a very successful approximate solution to the classic AI problem of programming intelligence." [[http://distancelearning.ksi.edu/demo/509/ch01a.html] Expert systems are a branch of Artificial Intelligence that makes extensive use of specialized knowledge to solve problems at the same level as a human expert. ...read more.

Middle

An expert system simply automates decision making and problem solving techniques in a domain which saves time and money as human intelligence is not required to be purchased to find a solution. Dependant on how the expert system is used is highly conclusive on how the expert system achieves its tasks, for example in the medical field an expert system is used as a decision support, this is where a conclusion is made in the doctor's brain, but is then checked and checked against the knowledge and experience of the expert system. The most supported and use expert system technique is that of 'decision making' this is where prior entered knowledge and expertise is used to train or 'make decisions' beyond the level or expertise of the user. Some expert systems however use set rules to determine an outcome, this is in the format of using IF...THEN...ELSE statements, this is where a decision is made or concluded in a particular state or situation. This is where conditions are checked against one another for example: IF X is not = 5 THEN X = 10 ELSE "invalid figure" Expert systems are made up on thousands of these statements, which are more commonly known as 'inference rules' Expert systems and their evolution have brought about a clever methodology in which the rule base is segmented, allowing for elimination of segments of knowledge/data that are not relevant to the application process in hand. ...read more.

Conclusion

This method shows the relationships between the different properties. This then all links together to form a large network, which in turn is the systems knowledge base. Another representation method is using systematic networks; they represent the path and sequential root that the expert would take to gain a conclusion or solution to a problem. It is very closely matched to that of how a human brain would think in order to reach a final conclusion. It works in a similar way to how a doctor would diagnose a patient. Define three types of human learning. Describe the differences between these learning methods and the 'learning' capabilities of an Expert System. Humans ultimately learn in three different ways: - Learning by experience Learning by experience is a significant sign of intelligence. Experts are often tested of their ability by simply doing recall tests, this is where they are asked questions in a domain that they are specialized in, and tests have shown that they have a much more rapid response time to the questions than that of non-experts. A massive rise in Artificial Intelligence brought around the wish for computers to be able to learn from their own mistakes and experiences, an example of this is an expert system called "SOAR" , SOAR is an expert system created by the Carnegie Mellon University and Michigan University, the main operation of the expert system is that of remembering lines of reasoning that have been successful previously, these stored items can later be recalled in similar situations. ...read more.

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