Of An Expert System

 Introduction  What is AI?  Intelligent in Human & Machine? What is Expert System? How are Expert System used? Elements of ES Who are people involved in an Expert System project ?  Comparison of expert systems with (( conventional systems and human experts ))  Knowledge Definition of Knowledge Knowledge acquisition Knowledge representation

 "Artificial intelligence is the study of how to make computers do things at which, at moment, people are better“ Elaine Rich (1983)  Artificial intelligence is the branch of computer science that focuses on the development of computer systems.  Artificial is also called machine intelligence

Computer Science

 Expert systems are artificial intelligence (AI) tools that capture the expertise of knowledge workers “Experts” and provide advice to (usually) non-experts in a given domain.  Expert systems are implemented with artificial intelligence technology, often called expert system shells.  Expert System Shell : is an expert system but without knowledge “with empty knowledge”

 Many expert systems are built using a generic ‘shell’. An expert system shell consists of the programming components of an expert system but without a KB. Using a shell, a knowledge engineer can quickly enter a new KB and, without the need for any programming, create a complete working expert system.  The expert system can be used many times with the same knowledge using that knowledge to solve different problems (just like a doctor uses their knowledge many times to diagnose and cure lots of patients).

The elements of an expert system are as follows:  Expert—human expert to provide the knowledge for the expert system.  Database—some knowledge acquisition methods use data in databases to automatically generate new rules, e.g. weather data can be used to generate rules that will enable prediction of tomorrow’s weather.  Acquisition module— obtains appropriate knowledge from the human expert and the database ready for input to the KB of the expert system.  Knowledge base— retains the knowledge and rules used by the expert system in making decisions.  Inference engine— system that reasons to provide answers to problems placed into the expert system. The inference engine uses knowledge from the KB to arrive at a decision.  Explanatory interface— to provide the user with an explanation on how the expert system reached its conclusion.  User—the human being using the expert system!

Knowledge is a theoretical or practical understanding of a subject or a domain. Who owns knowledge are called experts. Domain expert is anyone has deep knowledge and strong practical experience in a particular domain. An expert is a skilful person who can do things other people cannot. What is Knowledge ?

‘Knowledge engineering is the process of developing knowledge based systems in any field, whether it be in the public or private sector, in commerce or in industry’ (Debenham, 1988).  Knowledge engineering normally involves five distinct steps (listed below) in transferring human knowledge into some form of knowledge based system(KBS). 1. Knowledge acquisition 2. Knowledge validation 3. Knowledge representation 4. Inferencing 5. Explanation “Interface”

 Knowledge acquisition is the process of acquiring the knowledge from human experts or other sources (e.g. books, manuals) to solve the problem.  the knowledge acquisition process primarily involves a discussion between the knowledge engineer and the human expert.  A knowledge engineer can also use interviews as method of obtaining knowledge from human experts however they must also consider other sources of knowledge. (( records of past case studies, standards documentation,knowledge from other humans who are less knowledgeable but more available then experts. ))

 An Interview is the easiest technique for Knowledge Acquisition.  To conduct a successful interview the knowledge engineer will need to: plan use appropriate stage management techniques consider and use appropriate social skills maintain appropriate self-control during the interview.

 The interview normally consists of three parts :

Questions useful to begin the interview process include:  Can you give me an overview of the subject?  Can you describe the last case you dealt with?  What facts or hypotheses do you try to establish when thinking about a problem?  What kinds of things do you like to know about when you begin to think about a problem?  Leading on to find a little more detail; tell me more about how this is achieved?  What do you do next?  How does that relate to... ?  How, why, when, do you do that?  Can you describe what you mean by that? Closing an interview by reviewing the information obtained, and perhaps by alerting the expert to the need for further interviews, is also important.

 By knowledge engineer Tutorial interviews “presentation” Twenty question interviews “Yes or No” Teach back interviews “past interviews” Observation studies  Observation of an expert doing his task  The cooperation with the expert can be difficult  The time consuming for the knowledge engineer  No knowledge engineer necessary Machine induction “ automated Knowledge Acquisition “  Rules are automatically induced from given examples  Database is instable & Rules are complex

 Dealing with Multiple Experts