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.