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"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
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From the very beginning of artificial intelligence, search has been vital as a core technique in problem-solving. Heuristic Search provides 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.
Search as a problem-solving tool is shown 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 are included.
The content is organized into five parts as follows:
- Heuristic Search Primer: Basic Search Algorithms, Dictionary Data Structures, Automatically Created Heuristics
- Heuristic Search under Memory Constraints: Linear-Space Search, Memory-Restricted Search, Symbolic Search, External Search
- Heuristic Search Under Time Constraints: Distributed Search, State-Space Pruning, and Real-Time Search
- Heuristic Search Variants: Adversary Search, Constraint Search, and Selective Search
- Heuristic Search Applications: Action Planning, Automated System Verification, Vehicle Navigation, Computational Biology, and Robotics
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From the very beginning of artificial intelligence, search has been vital as a core technique in problem-solving.Heuristic Search provides 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.
Search as a problem-solving tool is shown 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 are included.
The content is organized into five parts as follows:
- Heuristic Search Primer: Basic Search Algorithms, Dictionary Data Structures, Automatically Created Heuristics
- Heuristic Search under Memory Constraints: Linear-Space Search, Memory-Restricted Search, Symbolic Search, External Search
- Heuristic Search Under Time Constraints: Distributed Search, State-Space Pruning, and Real-Time Search
- Heuristic Search Variants: Adversary Search, Constraint Search, and Selective Search
- Heuristic Search Applications: Action Planning, Automated System Verification, Vehicle Navigation, Computational Biology, and Robotics
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