Knowledge management in societies of intelligent adaptive agents

A model is developed of the emergence of the knowledge level in a society of agents where agents model and manage other agents as resources, and manage the learning of other agents to develop such resources. It is argued that any persistent system that actively creates the conditions for its persistence is appropriately modeled in terms of the rational teleological models that Newell defines as characterizing the knowledge level. The need to distribute tasks in agent societies motivates such modeling, and it is shown that if there is a rich order relationship of difficulty on tasks that is reasonably independent of agents then it is efficient to model agents competencies in terms of their possessing knowledge. It is shown that a simple training strategy of keeping an agent's performance constant by allocating tasks of increasing difficulty as an agent adapts optimizes the rate of learning and linearizes the otherwise sigmoidal learning curves. It is suggested that this provides a basis for assigning a granularity to knowledge that enables learning processes to be managed simply and efficiently. © 1997 Kluwer Academic Publishers.