Artificial Life Applied to Adaptive Information Agents

Filippo Menczer, Richard K. Belew, and Wolfram Willuhn

We propose a model, inspired by recent artificial life theory, applied to the problem of retrieving information from a large, distributed collection of documents such as the World Wide Web. A population of agents is evolved under density dependent selection for the task of locating information for the user. The energy necessary for survival is obtained from both environment and user in exchange for appropriate information. By competing for relevant documents, the agents robustly adapt to their information environment and are allocated to efficiently exploit shared resources. We illustrate the roles played by document locality, adaptive search strategies, and relevance feedback, in the information gathering process.


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