Preface This book is intended to serve as a comprehensive introduction to artificial intelligence (AI) heuristic state space search. It includes many developments that are not yet covered by any textbook, including pattern databases, symbolic search, search with efficient use of external memory, and parallel process- ing units. Thus, the book is suitable for readers who do not yet have a background in search and are looking for a good introduction to the field, as well as for readers who do have some background in search and are interested in reading up on the latest developments in the field. It serves as a unique reference to the large body of research and provides many pointers to the literature. The book is intended to strike a balance between search algorithms and their theoretical analysis on one hand, and their efficient implementation and application to important real-world problems on the other hand. It is intended to cover the field comprehensively, from well-known basic results to recent proposals that push the state of the art. The book supplements broad textbooks on AI and, more importantly, serves as a primary text- book for more advanced AI classes on search. It discusses search applications in a variety of subfields of AI, including puzzle solving, game playing, constraint satisfaction, action planning, and robotics. However, the book is also suitable for self-study and provides a valuable source of information for graduate students, researchers, and practitioners in a variety of decision sciences (including AI and operations research), as well as application programmers who need to use search algorithms to solve their problems. The book is relatively self-contained. In particular, we do not require the readers to have any prior knowledge of AI. Instead, we assume the reader to have basic knowledge of algorithms, data structures,