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Published in:
2011 | OriginalPaper | Chapter
Collaborative Redundant Agents: Modeling the Dependences in the Diversity of the Agents’ Errors
Authors : Laura Zavala, Michael Huhns, Angélica García-Vega
Published in: Advances in Artificial Intelligence
Publisher: Springer Berlin Heidelberg
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As computing becomes pervasive, there are increasing opportunities for building collaborative multiagent systems that make use of multiple sources of knowledge and functionality for validation and reliability improvement purposes. However, there is no established method to combine the agents’ contributions synergistically. Independence is usually assumed when integrating contributions from different sources. In this paper, we present a domain-independent model for representing dependences among agents. We discuss the influence that dependence-based confidence determination might have on the results provided by a group of collaborative agents. We show that it is theoretically possible to obtain higher accuracy than that obtained under the assumption of independence among the agents. We empirically evaluate the effectiveness of a collaborative multiagent system in the presence of dependences among the agents, and to analyze the effects of incorrect confidence integration assumptions.