This chapter discusses the acquisition of knowledge so that it can be encoded for use in decision support systems. This requires an understanding of both the factual knowledge, which is required to solve the relevant problems, and the judgmental knowledge, which characterizes a decision maker who gets to the heart of a problem effectively, discards irrelevant information, and demonstrates an ability to be creative rather than to solve problems by rote formula every time they arise. Knowledge acquisition (KA) occurs by interacting with human beings-analyzing their behaviors, inferring their beliefs and knowledge, asking them to explain their thought processes and actions, and applying formal or informal methods for extracting factual and judgmental knowledge, which they happen to apply from those behaviors and explanations. Such interactions can be undertaken by human beings interacting with experts or by computer programs that experts can use to convey what they know for capturing in a computer-based representation. The techniques and theories that enable KA are viewed within the context of the process that begins with the acquisition of knowledge from human experts, followed by the representation of that knowledge in a computationally tractable form that supports knowledge-based agents or applications. Users would then include the verification and validation of the output of those knowledge-based agents or applications as part of the KA process as they provide feedback regarding the quality of the contents of the underlying knowledge structures.
ASJC Scopus subject areas
- Business, Management and Accounting(all)