The Theory

Backward Chaining - Goal driven inferencing system that assumes a hypothesis of what the likely outcome will be, and then works backwards to collect the evidence to supportthis conclusion. Expert systems used for planning often use backward chaining.

Conflict Resolution - In a forward chaining system, many rules can fire at the same time (a rule can fire when all its attached conditions match the facts in the working memory). Conflict resolution provides methods of choosign which of the rules to fire.

Consultation - The running of an expert system to produce advice by the user answering questions.

Domain - Area of knowledge of an expert system.

Expert System Shell - Software with a builit-in inference engine, ready-made user interface and an empty knowledge base.

Forward Chaining - Inferencing method which gathers facts (like a detective at the scene of a crime) until enough evidence is collected that points to an outcome. Often used in expert systems for diagnosis, advise and classification, although size and complexity of the system can play a part in deciding which method of inferencing to use.

How Justification - Where the system justifies its reasoning for providing a piece of advice.

Inference Engine - Applies the facts to the rules and determines the questions to be asked of the user in the user interface and in which order to ask them. This is the 'invisible' part of the expert system, which is active during a consultaion of the system (when the user choses to run the program).

Knowledge Acquisition - The process of getting the knowledge from expert sources that will entered into the expert system, e.g. interviewing an expert, researching a topic.

Knowledge Base - Where information is stored in the expert system in the form of facts and rules (basically a series of IF statements). Where the programmer writes the code for the expert system.

Knowledge Representation - Once expeet knowledge is acquired, it should be presented in a form that makes it easy to construct facts and rules to be coded in the system. Common examples are factor tables and rule trees.

Knowledge Validation - Testing the completed expert system to see that the advice is generates matches the advice that would be produced by a human expert.

Knowledge Representation Language (KRL) - Solution used to code the facts and rules into the expert system, such as an expert system shell, a high level language or a specific artificial intelligence language.

Rule Ordering (First Come First Served) - Conflict resolution strategy where the first rule in the conflict set is fired. E.g. Rule1, Rule2 and Rule3 - fire Rule1.

Recency - Conflict reolution strategy where the rule is fired which matches the facts added most recently to the working memory.

Refractoriness - Conflict resolution strategy where the rule just fired is removed form the conlict set to ensure the system does not go into a continuous loop.

Rete Algorithm - Limits the effort required to recompute the conflict set after a rule is fired. The increased system efficiency comes with a cost regarding meory useage.

Specificity - Conflict reolution strategy where the rule with the most conditions attached to it is fired first.

User Interface - Where the user interacts with the expert system. I.e. where questions are asked, and advice is produced. As well as the advice that is output, the user interface can output the justification featutes of an expert system.

Why Justification - where the system justifies why a particular question is being asked.

Working Memory - Contains the facts received from the user via questions asked during a consulation session with the expert system.