The main purpose of this book is to describe several aspects of combinatorial search algorithms and systems in a unified manner.

The book consists of eight chapters, divided into two main parts: chapters 2 through 4 discuss distributed and parallel algorithms, while chapters 5 and 6 describe the advantages of using learning mechanisms during the search process. Chapter 7 unifies all of the previous chapters into a novel autonomous search framework, and provides a taxonomy of the search methods with respect to their computation features. Finally, the last chapter presents conclusions and perspectives.

Combinatorial search algorithms are designed to efficiently explore the large solution space of complex optimization problems. This topic represents a very important research direction for combinatorial optimization. Based on combinatorial search, efficient and powerful algorithms may be designed for solving nondeterministic polynomial-time (NP)-hard optimization problems, which have many applications in fields such as transportation, telecommunications, computer networking, financial planning, logistics, and energy distribution.

The author describes several search methods that focus on the constraint satisfaction problem (CSP) and the prepositional satisfiability problem (SAT). Several combinatorial search strategies and algorithms are described, such as a generic framework for the execution of algorithms for the distributed constraint satisfaction problem (DisCSP), a portfolio-based parallel SAT solver called ManySAT, sharing strategies in parallel local search for SAT, and a continuous search framework.

The book is well written and will be useful for researchers and graduate students in computer science, operations research, and applied mathematics. Due to the considerable practical importance of the presented topics, researchers from other areas related to combinatorial optimization will find it helpful as well.