Rule base is a list of if-then rules which are interconnected. A semantic net represents knowledge and expertise that is composed of easily identified chunks or objects of hierarchically interrelated characteristics. Semantic nets are more efficient than rules if the data can be represented this way. Frames also organize knowledge into chunks, but chunks that are related by shared characteristics. For example, cars and tanks are separate frames.
Inference engine is the software that embodies the strategy used to search through the rule base.
The system searches for stored cases similar to the new one, locates the closest fit, and offers the solution to the old case for use with the new case. If the new case fits the solution, it is added to the case database. If not, the case will be added with a new solution or explanations as to why the solutions didn't work. CBR's differ from expert systems in that they capture the knowledge of the organization rather than a single expert, and the knowledge is captured as cases rather than if-then rules. Describe three problems of expert systems.