dc.description.abstract | Knowledge Management Systems (KMS) are important tools by which
organizations can better use information and, more importantly, manage
knowledge. Unlike other strategies, knowledge management (KM) is difficult to
define because it encompasses a range of concepts, management tasks,
technologies, and organizational practices, all of which come under the umbrella of
the information management. Semantic approaches allow easier and more efficient
training, maintenance, and support knowledge. Current ICT markets are dominated
by relational databases and document-centric information technologies, procedural
algorithmic programming paradigms, and stack architecture. A key driver of global
economic expansion in the coming decade is the build-out of broadband
telecommunications and the deployment of intelligent services bundling. This paper
introduces the main characteristics of an Intelligent Knowledge Management
System as a multiagent system used in a Learning Control Problem (IKMSLCP),
from a semantic perspective. We describe an intelligent KM framework, allowing
the observer (a human agent) to learn from experience. This framework makes the
system dynamic (flexible and adaptable) so it evolves, guaranteeing high levels of
stability when performing his domain problem P. To capture by the agent who learn
the control knowledge for solving a task-allocation problem, the control expert
system uses at any time, an internal fuzzy knowledge model of the (business)
process based on the last knowledge model. | en_US |