Performance management in competitive distributed Web search

Distributed heterogeneous search environments are an emerging phenomenon in Web search. We provide a theoretical analysis of the problem and propose a method, utilising reinforcement learning techniques, for automatically managing search engine content. We examine the problem of performance-maximising behaviour for noncooperative specialised search engines in heterogeneous search environments. In particular, we analyse a scenario in which individual search engines compete for queries by choosing which documents to index. We provide game-theoretic analysis of a simplified version of the problem and motivate the use of the concept of "bounded rationality". We then cast our problem as a reinforcement learning task, where the goal of a specialised search engine is to exploit suboptimal behaviour of its competitors to improve own performance.