Heuristic Search
Theory and Applications

Your guide to the analysis, implementation and application of heuristic search for artificial intelligence problem-solving techniques

Stefan Edelkamp (Author), Stefan Schroedl (Author)

9780123725127

Hardback, published 28 July 2011

712 pages
23.5 x 19 x 3.7 cm, 1.6 kg

"Heuristic Search is a very solid monograph and textbook on (not only heuristic) search. In its presentation it is always more formal than colloquial, it is precise and well structured. Due to its spiral approach it motivates reading it in its entirety." --Zentralblatt MATH 2012

"The authors have done an outstanding job putting together this book on artificial intelligence (AI) heuristic state space search. It comprehensively covers the subject from its basics to the most recent work and is a great introduction for beginners in this field." --BCS.org

"Heuristic search lies at the core of Artificial Intelligence and it provides the foundations for many different approaches in problem solving. This book provides a comprehensive yet deep description of the main algorithms in the field along with a very complete discussion of their main applications. Very well-written, it embellishes every algorithm with pseudo-code and technical studies of their theoretical performance." --Carlos Linares López, Universidad Carlos III de Madrid

"This is an introduction to artificial intelligence heuristic state space search. Authors Edelkamp (U. of Bremen, Germany) and Schrödl (a research scientist at Yahoo! Labs) seek to strike a balance between search algorithms and their theoretical analysis, on the one hand, and their efficient implementation and application to important real-world problems on the other, while covering the field comprehensively from well-known basic results to recent work in the state of the art. Prior knowledge of artificial intelligence is not assumed, but basic knowledge of algorithms, data structures, and calculus is expected. Proofs are included for formal rigor and to introduce proof techniques to the reader. They have organized the material into five sections: heuristic search primer, heuristic search under memory constraints, heuristic search under time constraints, heuristic search variants, and applications." --SciTech Book News

"This almost encyclopedic text is suitable for advanced courses in artificial intelligence and as a text and reference for developers, practitioners, students, and researchers in artificial intelligence, robotics, computational biology, and the decision sciences. The exposition is comparable to texts for a graduate-level or advanced undergraduate course in computer science, and prior exposure or coursework in advanced algorithms, computability, or artificial intelligence would help a great deal in understanding the material. Algorithms are described in pseudocode, accompanied by diagrams and narrative explanations in the text. The vast size of the ‘search algorithms’ subject domain and the variety of applications of search mean that much information--especially pertaining to applications of search algorithms--had to be left out; however, an extensive (though still limited) bibliography is included for follow-up by the reader. Exercises are provided for each chapter, except the five chapters on applications, and bibliographic notes accompany all chapters." --Computing Reviews

Search has been vital to artificial intelligence from the very beginning as a core technique in problem solving. The authors present a thorough overview of heuristic search with a balance of discussion between theoretical analysis and efficient implementation and application to real-world problems. Current developments in search such as pattern databases and search with efficient use of external memory and parallel processing units on main boards and graphics cards are detailed.

Heuristic search as a problem solving tool is demonstrated in applications for puzzle solving, game playing, constraint satisfaction and machine learning. While no previous familiarity with heuristic search is necessary the reader should have a basic knowledge of algorithms, data structures, and calculus. Real-world case studies and chapter ending exercises help to create a full and realized picture of how search fits into the world of artificial intelligence and the one around us.

PART I Heuristic Search Primer Chapter 1 Introduction Chapter 2 Basic Search Algorithms Chapter 3 Dictionary Data Structures Chapter 4 Automatically Created Heuristics

PART II Heuristic Search under Memory Constraints Chapter 5 Linear-Space Search Chapter 6 Memory Restricted Search Chapter 7 Symbolic Search Chapter 8 External Search

PART III Heuristic Search under Time Constraints Chapter 9 Distributed Search Chapter 10 State Space Pruning Chapter 11 Real-Time Search by Sven Koenig

PART IV Heuristic Search Variants Chapter 12 Adversary Search Chapter 13 Constraint Search Chapter 14 Selective Search

PART V Heurstic Search Applications Chapter 15 Action Planning Chapter 16 Automated System Verification Chapter 17 Vehicle Navigation Chapter 18 Computational Biology Chapter 19 Robotics by Sven Koenig

Subject Areas: Machine learning [UYQM], Expert systems / knowledge-based systems [UYQE], Artificial intelligence [UYQ]