Distributed Artificial Intelligence
Distributed Artificial Intelligence
Social and cognitive processes in knowledge acquisition
Knowledge Acquisition
The collective stance in modeling expertise in individuals and organizations
International Journal of Expert Systems
Distributed artificial intelligence: theory and praxis
Distributed artificial intelligence: theory and praxis
Reasoning about knowledge
Rise of the Network Society
Artificial Life: An Overview
Industrial Applications of Fuzzy Control
Industrial Applications of Fuzzy Control
Architecture of Systems Problem Solving
Architecture of Systems Problem Solving
Cybernetics: Or Control and Communication in Animal and the Machine
Cybernetics: Or Control and Communication in Animal and the Machine
Modeling adaptive autonomous agents
Artificial Life
Conceptual Models and Architectures for Advanced Information Systems
Applied Intelligence
Workflow-and agent-based cognitive flow management for distributed team cooperation
Information and Management
A grounded theory of the flow experiences of web users
International Journal of Human-Computer Studies - Incorporating knowledge acquisition
An agency-based framework for electronic business
CIA'99 Proceedings of the 3rd international conference on Cooperative information agents III
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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.