No index database is built, eliminating the problems of size, server load, and dynamic updating. The search process makes use of the WWW hyperstructure to carry out the search, and dynamically adapts to its changes as well as to the variability due to different users and queries. No overhead is incurred by explicit communication among agents, while competition for shared resources intrinsically enforces a balanced network load. Exhaustive search is overcome by more efficient, adaptive branch cutting in the search space. Current goals are evaluating how performance scales with search space size, as well as comparisons with alternative search methods.
Many extensions are possible for improved implementations of our model.
Including server information in the computation of energy costs allows better exploitation of network resources. Smart convergence measures may speed up the location of information-rich areas in the search graph. Finally, user feedback may drastically improve on-line performance by allowing to shape the adaptive landscape without detailed knowledge about its structure. A Markovian extension of Valiant's learning model. 31st Annual Symposium on Foundations of Computer Science, 392-6.