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IJCAI 2009

Conference Paper Agent-based and Multiagent Systems Artificial Intelligence

Abstract

Recently, online reputation mechanisms have been proposed that reward agents for honest feedback about products and services with fixed quality. Many real-world settings, however, are inherently dynamic. As an example, consider a web service that wishes to publish the expected download speed of a file mirrored on different server sites. In contrast to the models of Miller, Resnick and Zeckhauser and of Jurca and Faltings, the quality of the service (e. g. , a server’s available bandwidth) changes over time and future agents are solely interested in the present quality levels. We show that hidden Markov models (HMM) provide natural generalizations of these static models and design a payment scheme that elicits honest reports from the agents after they have experienced the quality of the service.

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Context

Venue
International Joint Conference on Artificial Intelligence
Archive span
1969-2025
Indexed papers
14525
Paper id
931904140180971846
v2026.09.13