Book description
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.
- Provides real-world success stories and case studies for heuristic search algorithms
- Includes many AI developments not yet covered in textbooks such as pattern databases, symbolic search, and parallel processing units
Table of contents
- Cover Image
- Table of Contents
- Front Matter
- Copyright
- List of Algorithms
- Preface
- Chapter 1. Introduction
- Chapter 2. Basic Search Algorithms
- Chapter 3. *Dictionary Data Structures
- Chapter 4. Automatically Created Heuristics
-
Chapter 5. Linear-Space Search
- 5.1. *Logarithmic Space Algorithms
- 5.2. Exploring the Search Tree
- 5.3. Branch-and-Bound
- 5.4. Iterative-Deepening Search
- 5.5. Iterative-Deepening A*
- 5.6. Prediction of IDA* Search
- 5.7. *Refined Threshold Determination
- 5.8. *Recursive Best-First Search
- 5.9. Summary
- 5.10. Exercises
- 5.11. Bibliographic Notes
- Chapter 6. Memory-Restricted Search
-
Chapter 7. Symbolic Search
- 7.1. Boolean Encodings for Set of States
- 7.2. Binary Decision Diagrams
- 7.3. Computing the Image for a State Set
- 7.4. Symbolic Blind Search
- 7.5. Limits and Possibilities of BDDs
- 7.6. Symbolic Heuristic Search
- 7.7. * Refinements
- 7.8. Symbolic Algorithms for Explicit Graphs
- 7.9. Summary
- 7.10. Exercises
- 7.11. Bibliographic Notes
- Chapter 8. External Search
- Chapter 9. Distributed Search
- Chapter 10. State Space Pruning
- Chapter 11. Real-Time Search
- Chapter 12. Adversary Search
- Chapter 13. Constraint Search
-
Chapter 14. Selective Search
- 14.1. From State Space Search to Minimization
- 14.2. Hill-Climbing Search
- 14.3. Simulated Annealing
- 14.4. Tabu Search
- 14.5. Evolutionary Algorithms
- 14.6. Approximate Search
- 14.7. Randomized Search
- 14.8. Ant Algorithms
- 14.9. * Lagrange Multipliers
- 14.10. * No-Free-Lunch
- 14.11. Summary
- 14.12. Exercises
- 14.13. Bibliographic Notes
- Chapter 15. Action Planning
- Chapter 16. Automated System Verification
- Chapter 17. Vehicle Navigation
- Chapter 18. Computational Biology
- Chapter 19. Robotics
- Bibliography
- Index
Product information
- Title: Heuristic Search
- Author(s):
- Release date: May 2011
- Publisher(s): Morgan Kaufmann
- ISBN: 9780080919737
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