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Coherent cooperation among communicating problem solvers
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Distributed Artificial Intelligence
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EWSL-91 Proceedings of the European working session on learning on Machine learning
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An architecture for adaptive intelligent systems
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Foundations of distributed artificial intelligence
The Hearsay-II Speech-Understanding System: Integrating Knowledge to Resolve Uncertainty
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A Framework for the Analysis of Sophisticated Control in Interpretation Systems
A Framework for the Analysis of Sophisticated Control in Interpretation Systems
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In this paper we describe a dynamic, adaptive communication strategy for multiagent systems. We discuss the behavioral parameters ofeach agent that need to be computed, and provide a quantitative solution tothe problem of controlling these parameters. We also describe the testbedwe built and the experiments we performed to evaluate the effectiveness ofour methodology. Several experiments using varying populations and varyingorganizations of agents were performed and are reported. A number ofperformance measurements were collected as each experiment was performed sothe effectiveness of the adaptive communications strategy could be measuredquantitatively.The adaptive communications strategy proved effective for fully connectednetworks of agents. The performance of these experiments improved forlarger populations of agents and even approached optimal performancelevels. Experiments with non-fully connected networks showed that theadaptive communications strategy is extremely effective, but does notapproach optimality. Other experiments investigated the ability of theadaptive communications strategy to compensate for“distracting” agents, for systems where agents are required toassume the role of information routers, and for systems that must decidebetween routing paths based on cost information.