Saturday, June 23, 2007

Structure of ES

Structure of the Expert System

The main elements of the Expert System are show in Figure:
 

1. User interface
2. Knowledge Base
3. Inference Engine
4. Knowledge Acquisition
5. Working Memory
6. Explanation Facility Knowledge



Inference Engine
The inference engine connects the knowledge base and the database of facts. It interprets the rules and draws conclusions. With rule based expert systems there are two main types of reasoning - forward chaining and backward chaining. The user interface for both of these systems may be similar, it is how they use the rules that is different. Some expert systems use both backward and forward chaining.

Forward chaining
Forward chaining is a 'data driven' method of reasoning. It begins with the available data, compares it with the facts and rules held in the knowledge base and then infers or draws the most likely conclusion. IF -  THEN. Forward chaining starts with the symptoms and works forward to find a solution.
Backward chaining
Backward chaining is a 'goal driven' method of reasoning. It begins with a goal and then looks at the evidence (data and rules) to determine whether or not it is correct. THEN -IF. Backward chaining starts with a hypothesis and works backwards to prove or disprove it.



The Knowledge Engineer
The knowledge engineer is concerned with the representation chosen for the expert's knowledge declarations and with the inference engine used to process that knowledge. There are several characteristics known to be appropriate to a good inference technique:
  1. A good inference technique is independent of the problem domain. In order to realize the benefits of explanation, knowledge transparency, and re-usability of the programs in a new problem domain, the inference engine must contain domain specific expertise.
  2. Inference techniques may be specific to a particular task, such as diagnosis of hardware configuration. Other techniques may be committed only to a particular processing technique.
  3. Inference techniques are always specific to the knowledge structures.
  4. Successful examples of rule processing techniques include: Forward chaining and Backward chaining



Reference:
- Inference engine by Charles Sturt University
- i2osig.org