Knowledge Management in Societies of Intelligent Adaptive Agents
A model is developed of the emergence of the knowledge level in asociety of agents where agents model and manage other agents as resources,and manage the learning of other agents to develop such resources. It isargued that any persistent system that actively creates the conditions forits persistence is appropriately modeled in terms of the rationalteleological models that Newell defines as characterizing the knowledgelevel. The need to distribute tasks in agent societies motivates suchmodeling, and it is shown that if there is a rich order relationship ofdifficulty on tasks that is reasonably independent of agents then it isefficient to model agents competencies in terms of their possessingknowledge. It is shown that a simple training strategy of keeping an agent'sperformance constant by allocating tasks of increasing difficulty as anagent adapts optimizes the rate of learning and linearizes the otherwisesigmoidal learning curves. It is suggested that this provides a basis forassigning a granularity to knowledge that enables learning processes to bemanaged simply and efficiently.