This book frames a peer-to-peer information retrieval
problem as a multi-agent framework and attacks it
from an organizational perspective by exploring
various adaptive, self-organizing topological
organizations, designing appropriate
coordination strategies, and exploiting learning
techniques to create more accurate routing policy for
large-scale agent organizations. In addition, a
reinforcement-learning based approach is developed in
this thesis to take advantage of the run-time
characteristics of P2P IR systems, including
environmental parameters, bandwidth usage, and
historical information about past search sessions. In
the learning process, agents refine their content
routing policies by constructing relatively accurate
routing tables based on a Q-learning algorithm.
Experimental results show that this learning
algorithm considerably improves the performance of
distributed search sessions in P2P IR systems.

The book is addressed to researchers and
practitioners in information retrieval and search
engine, content-based routing areas.