ABSTRACT
Knowledge representation in expert systems deals with structures used to represent the knowledge provided by experts. Efficient knowledge representation is key to the success of the overall expert system. Through the use of an appropriate representation, knowledge can be manipulated effectively and precisely so that an expert system can arrive at correct conclusions. This chapter explores the implementation of learning in a rule-based knowledge base. The learning system for grammars and lexicons (LSGL) facility within the fuzzy expert system tools (FEST) shell provides the capability to modify and create knowledge structures for knowledge bases in expert systems. This capability allows the expert system shell to process knowledge bases with different structures, thereby increasing the adaptability and processing power of the expert system.
