Exploiting multi-agent interactions for identifying the best-payoff information source

In many different applications on the Web, distributed agents would like to discover and access high quality information sources. This is a challenging problem since an agent does not know a priori which information source would provide high quality information for particular topics. In this paper, we utilize machine learning techniques to allow a set of distributed agents to use their past experience and collaborate with others to identify information sources with the best payoff. The proposed method allows an individual agent to estimate the next payoff based on its own history of interactions with the information source and also on collaboration with other agents whose individual analysis of the next payoff the agent trusts. Q-learning is applied for stochastic updates to the payoff. Experimental results show that the proposed method provides the best results when an individual agent collaborates with a moderate number of neighbors.