Facilitating knowledge sharing in organizations: Semiautonomous agents that learn to gather, classify, and distribute environmental scanning knowledge.

Abstract

Evaluating patterns of indicators is often the first step an organization takes in scanning the environment. Not surprisingly, the experts that evaluate these patterns are not equally adept across all disciplines. While one expert is particularly skilled at recognizing the potential for political turmoil in a foreign nation, another is best at recognizing how Japanese government de-regulation is meant to complement the development of some new product. Moreover, the experts often benefit from one another's skills and knowledge in assessing activity in the environment external to the organization. One problem in this process occurs when the expert is unavailable and can't share his knowledge. And, addressing the problem of knowledge sharing, of distributing expertise, is the focus of this dissertation. A technical approach is adapted in this effort--an architecture and a prototype are described that provide the capability of capturing, organizing, and delivering the knowledge used by experts in classifying patterns of qualitative indicators about the business environment. Using a combination of artificial intelligence and machine learning techniques, a collection of objects termed "Apprentices" are employed to do the work of gathering, classifying, and distributing the expertise of knowledge workers in environmental scanning. Furthermore, an archival case study is provided to illustrate the operations of an Apprentice using "real world" data.