Competitive Markov decision processes
Competitive Markov decision processes
GlOSS: text-source discovery over the Internet
ACM Transactions on Database Systems (TODS)
Proceedings of the 1st ACM conference on Electronic commerce
The invisible Web: uncovering information sources search engines can't see
The invisible Web: uncovering information sources search engines can't see
Information Retrieval
Learning to Cooperate via Policy Search
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Reinforcement learning by policy search
Reinforcement learning by policy search
Rational and convergent learning in stochastic games
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Learning models of intelligent agents
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Learning about other agents in a dynamic multiagent system
Cognitive Systems Research
Specialisation dynamics in federated web search
Proceedings of the 6th annual ACM international workshop on Web information and data management
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Distributed heterogeneous search systems are an emerging phenomenon in Web search, in which independent topic-specific search engines provide search services, and metasearchers distribute user's queries to only the most suitable search engines. Previous research has investigated methods for engine selection and merging of search results (i.e. performance improvements from the user's perspective). We focus instead on performance from the service provider's point of view (e.g, income from queries processed vs. resources used to answer them). We consider a scenario in which individual search engines compete for user queries by choosing which documents (topics) to index. The difficulty here stems from the fact that the utilities of local engine actions should depend on the uncertain actions of competitors. Thus, naive strategies (e.g, blindly indexing lots of popular documents) are ineffective. We model the competition between search engines as a stochastic game, and propose a reinforcement learning approach to managing search index contents. We evaluate our approach using a large log of user queries to 47 real search engines.