Showing papers on "Knowledge sharing published in 1990"

Toward successful implementation of knowledge-based systems: expert systems versus knowledge sharing systems

Abstract: Strategies are presented for successful implementation of knowledge-based systems in management fields where expertise is decentralized. The crucial role of the knowledge flow from knowledge suppliers through knowledge-based systems to system users in successful implementation of knowledge-based systems is discussed. This leads to a presentation of two paradigms, namely consulting paradigms and knowledge-sharing paradigms for knowledge-based systems, followed by identification of major implementation requirements for those two paradigms. Strategies for implementation of knowledge-based systems are then presented for expert systems that use the knowledge-sharing paradigm. >

Supporting knowledge sharing in environmental scanning

Abstract: An architecture and a prototype are described that provide the capability of capturing, organizing, and distributing knowledge that may be used by experts in classifying patterns of often qualitative indicators in the business environment. The establishment and application are described of a collection of semiautonomous agents, termed apprentices, that use a combination of artificial intelligence and machine learning techniques to do the work of gathering, classifying, and distributing the expertise of knowledge workers engaged in business-environment scanning. >

Distributed real-time expert control system

Abstract: A novel distributed multiprocessing architecture for a real-time expert-based process control system is considered. A knowledge-distributed model for dynamic process control is developed. A dynamic abstraction algorithm to configure online knowledge bases and schedule inference activities which are distributed over the subexperts is described. Each subexpert focuses on a specific problem domain and operates in parallel. A novel knowledge sharing structure based on content addressable memory (CAM) to implement a novel concept called knowledge sharing with inference is explored to address time-critical intelligent control applications. An online learning and fault-tolerant structure is given for the system robustness. >

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

A computational view of group problem saving

Abstract: A problem arising out of group decision support research is to develop a method to specify and represent the important phenomena comprising the events of the participants (humans and machines). The authors propose a solution in the form of a computational model of group problem solving derived from a view of individual problem solving. They view group problem solvers from the perspective of their capacities to function as intelligent agents; that is, they may be either humans or machines. They argue that the group tasks in which such intelligent agents engage, such as 'negotiation' or 'decision making', are actually specific forms of problem solving, and that the cognitive mechanisms underlying 'group problem solving' are precisely the same as those comprising individual problem solving. One mechanism central to the model is that of learning; therefore, the authors discuss the role learning plays in group communication and coordination. In order to test one aspect of the group model, they conducted an experiment in knowledge sharing among an assemblage of artificial-intelligence scheduling agents. The results indicate that knowledge sharing can be quite beneficial, but that the effects vary according to specific task environment experiences. >

New-category knowledge systems for human-computer cooperation

Abstract: Two categories of knowledge-based systems are proposed. These are knowledge sharing systems and knowledge communication systems. The essence of the knowledge sharing systems is that knowledge suppliers are the system users, thereby encouraging the users to share and use knowledge that they have provided. Knowledge communication systems provide users with unexpected knowledge that is chosen from the knowledge base by the user's colleagues. Knowledge communication systems use not only the user's intuitive judgment, but also the colleague's intuitive judgment to connect independent pieces of knowledge. Since knowledge sharing systems require knowledge variety, implementation strategies involve approaching large domains and applying a give-and-take principle to acquire knowledge. In knowledge communication systems, establishment of a colleague (user) database is an important task. >