ABSTRACT
Acquisition of knowledge is equally hard for machines as it is for the human beings. The chapter provides various tools and techniques for manual and automated acquisition of knowledge. Special emphasis is given to knowledge acquisition from multiple experts. A structured approach to knowledge refinement using fuzzy Petri nets has also been presented in the chapter. The proposed method analyzes the known case histories to refine the parameters of the knowledge base and combines them for their usage in a new problem. The chapter concludes with the justification of the reinforcement learning and the inductive logic programming in automated acquisition of knowledge.
