How Adaptive Agents Learn to Deal with Incomplete Queries in Distributed Environments



Queries that are not indicative of real information needs are a major problem for information retrieval systems. In this work we study how individual learning helps adaptive agents, when searching for information in a distributed environment, to modify incomplete queries in order to improve their retrieving performance. Two learning procedures, occurring in two different levels, will be proposed and their effect will be studied in several situations. Preliminary results show that changes induced by learning in the query vector of adaptive agents, provide an important advantage and enable them to make correct decisions about how to deal with this problem.

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