Multi-agent Instructional Communities: A Computational and Experimental Approach

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The Web provides thousands of resources for thousands of communities. However, these resources are difficult to locate and do not coordinate, resulting in unproductive experiences for users. The PIs propose an agent-based framework to reason about and retrieve appropriate materials, providing meaningful user guidance. These agents will be in the area of instructional technology and will employ user modeling and planning to provide robust performance despite changing availability of resources, modifications in the network configuration and variable user needs. Agents will communicate and select specialized learning resources to solve aspects of larger learning problems. They will negotiate distribution of learning tasks among available resources ensuring that all aspects of the total problem are addressed in a coordinated manner. A simulated large-scale on-line learning community will be constsructed to demonstrate the intended behavior. The simulation will simplify learning and tutoring yet will include conflict resolution and propose methods to avoid failure. It will illustrate qualitative, quantitative and visual benefits of the integrated community and explore the properties of on-line communities, anticipating and avoiding problems that arise as massive on-line learning communities develop. The PIs will demonstrate which techniques are productive, which impractical and which are scaleable in a massive network.