Key Features
*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
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.
Readership
Researchers, professors, and graduate students
Heuristic Search, 1st Edition
I Heuristic Search Primer
1 Introduction
1.1 Notational and Mathematical Background
1.2 Search
1.3 Success Stories
1.4 State Space Problems
1.5 Problem Graph Representations
1.6 Heuristics
1.7 Examples of Search Problems
1.8 General State Space Descriptions
1.9 Summary
1.10 Exercises
1.11 Bibliographic Notes
2 Basic Search Algorithms
2.1 Uninformed Graph Search Algorithms
2.2 Informed Optimal Search
2.3 General Weights
2.4 Summary
2.5 Exercises
2.6 Bibliographic Notes
3 Dictionary Data Structures
3.1 Priority Queues
3.2 Hash Tables
3.3 Subset Dictionaries
3.4 String Dictionaries
3.5 Summary
3.6 Exercises
3.7 Bibliographic Notes
4 Automatically Created Heuristics
4.1 Abstraction Transformations
4.2 Valtorta’s Theorem
4.3 Hierarchical A
4.4 Pattern Databases
4.5 Customized Pattern Databases
4.6 Summary
4.7 Exercises
4.8 Bibliographic Notes
II Heuristic Search under Memory Constraints
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
6 Memory Restricted Search
6.1 Linear Variants using Additional Memory
6.2 Non-Admissible Search
6.3 Reduction of the Closed List
6.4 Reduction of the Open List
6.5 Summary
6.6 Exercises
6.7 Bibliographic Notes
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
8 External Search
8.1 Virtual Memory Management
8.2 Fault Tolerance
8.3 Model of Computation
8.4 Basic Primitives
8.5 External Explicit Graph Search
8.6 External Implicit Graph Search
8.7 Refinements
8.8 External Value Iteration
8.9 Flash Memory
8.10 Summary
8.11 Exercises
8.12 Bibliographic Notes
III Heuristic Search under Time Constraints
9 Distributed Search
9.1 Parallel Processing
9.2 Parallel Depth-First Search
9.3 Parallel Best-first Search Algorithms
9.4 Parallel External Search
9.5 Parallel Search on the GPU
9.6 Bidirectional Search
9.7 Summary
9.8 Exercises
9.9 Bibliographic Notes
10 State Space Pruning
10.1 Admissible State Space Pruning
10.2
10.3 Summary
10.4 Exercises
10.5 Bibliographic Notes
11 Real-Time Search by Sven Koenig
11.1 LRTA
11.2 LRTA with Lookahead One
11.3 Analysis of the Execution Cost of LRTA
11.4 Features of LRTA
11.5 Additional Variants of LRTA
11.6 Examples for How to Use Real-Time Search
11.7 Summary
11.8 Exercises
11.9 Bibliography
IV Heuristic Search Variants
12 Adversary Search
12.1 Two-Player Games
12.2 Multi-Player Games
12.3 General Game Playing
12.4 AND/OR Graph Search
12.5 Summary
12.6 Bibliographic Notes
13 Constraint Search
13.1 Constraint Satisfaction
13.2 Consistency
13.3 Search Strategies
13.4 NP-hard Problem Solving
13.5 Temporal Constraint Networks
13.6 Path Constraints
13.7 Soft and Preference Constraints
13.8 Constraint Optimization
13.9 Summary
13.10 Exercises
13.11 Bibliographic Notes
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
14.11Summary
14.12 Exercises
14.13 Bibliographic Notes .
V Heurstic Search Applications
15 Action Planning
15.1 Optimal Planning
15.2 Suboptimal Planning
15.3 Bibliographic Notes
16 Automated System Verification
16.1 Model Checking .
16.2 Communication Protocols
16.3 Program Model Checking
16.4 Analyzing Petri Nets
16.5 Exploring Real-Time Systems
16.6 Analyzing Graph Transition Systems
16.7 Anomalies in Knowledge Bases
16.8 Diagnosis
16.9 Automated Theorem Proving
16.10 Bibliographic Notes
17 Vehicle Navigation
17.2 Routing Algorithms
17.3 Cutting Corners
17.4 Bibliographic Notes
18 Computational Biology
18.1 Biological Pathway
18.2 Mulitple Sequence Allignment
18.3 Bibliographic Notes
19 Robotics by Sven Koenig
19.1 Search Spaces
19.2 Search with Incomplete Information
19.3 Fundamental Robot-Navigation Tasks
19.4 Planning
19.5 Bibliographic Notes
