SIGMA: Integrating Learning Techniques in Computational Markets for Information Filtering

Grigoris J. Karakoulas and Innes A. Ferguson

This paper presents an adaptive model for multi-agent learning based on the metaphor of economic markets, that can cope with the non-stationary and partially observable nature of an information filtering task. Various learning and adaptation techniques - i.e. reinforcement learning, bidding price adjustmeat and relevance feedback - are integrated into the model. As a result of this integration learning through the model exploits market competition in order to dynamically consU'uct mixtures of local experts from selfish agents. The model is embedded into SIGMA (System of Information Gathering Market-basod Agents) for information filtering of Useaet netnews. The functionality of the system is discussed together with work underway for its evaluation.

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