Edelkamp, Stefan. Heuristic search: theory and applications / Stefan Edelkamp, Stefan Schrödl. p. cm. ISBN (hardback: acid-free paper). 1. Heuristic Search - 1st Edition - ISBN: , View on Theory and Applications DRM-free (EPub, PDF, Mobi). Editorial Reviews. Review. "Heuristic Search is a very solid monograph and textbook on (not only heuristic) search. In its presentation it is always more formal .
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Library of Congress Cataloging-in-Publication Data Edelkamp, Stefan. Heuristic search: theory and applications / Stefan Edelkamp, Stefan Schr¨odl. p. cm. Heuristics by Judea Pearl; a a comprehensive introduction to AI search In this text book, we study the theory and the applications of search algorithms. The. Heuristic Search. Theory and Applications. Stefan Edelkamp. Stefan Schrodl. AMSTERDAM • BOSTON • HEIDELBERG • LONDON. NEW YORK • OXFORD.
Heuristic Search: Theory and Applications PDF Download 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.
Hill climbing with a twist: - allow some moves downhill to worse states - start out allowing large downhill moves to much worse states and gradually allow only small downhill moves. The search initially jumps around a lot, exploring many regions of the state space. The jumping is gradually reduced and the search becomes a simple hill climb search for local optimum. Modified State Evaluation: Value of each state is a combination of: - the cost of the path to the state - estimated cost of reaching a goal from the state.
The idea is to use the path to a state to determine partially the rank of the state when compared to other states. Create a priority queue of search nodes initially the start state.
Priority is determined by the function f 2. While queue not empty and goal not found: get best state x from the queue. If x is not goal state: generate all possible children of x and save path information with each node.
Apply f to each new node and add to queue. Remove duplicates from queue using f to pick the best. Summary 6. Exercises 6. Bibliographic Notes Chapter 7. Symbolic Search 7.
Boolean Encodings for Set of States 7. Binary Decision Diagrams 7. Computing the Image for a State Set 7.
Symbolic Blind Search 7. Limits and Possibilities of BDDs 7. Symbolic Heuristic Search 7. Refinements 7. Symbolic Algorithms for Explicit Graphs 7.
Summary 7. Exercises 7. Bibliographic Notes Chapter 8. External Search 8. Virtual Memory Management 8. Fault Tolerance 8. Model of Computation 8. Basic Primitives 8.
External Explicit Graph Search 8. External Implicit Graph Search 8. Refinements 8. External Value Iteration 8. Flash Memory 8.
Summary 8. Exercises 8. Bibliographic Notes Chapter 9. Distributed Search 9. Parallel Processing 9. Parallel Depth-First Search 9.
Parallel Best-First Search Algorithms 9. Parallel External Search 9. Parallel Search on the GPU 9. Bidirectional Search 9.
Summary 9. Exercises 9. Bibliographic Notes Chapter State Space Pruning Admissible State Space Pruning Nonadmissible State Space Pruning Summary Exercises Real-Time Search LRTA Features of LRTA Variants of LRTA How to Use Real-Time Search Adversary Search Two-Player Games Multiplayer Games General Game Playing Constraint Search Constraint Satisfaction Consistency Search Strategies NP-Hard Problem Solving Temporal Constraint Networks Path Constraints Soft and Preference Constraints Constraint Optimization Selective Search From State Space Search to Minimization Hill-Climbing Search Simulated Annealing Tabu Search Evolutionary Algorithms Approximate Search Randomized Search Ant Algorithms Lagrange Multipliers Action Planning Optimal Planning Suboptimal Planning By contrast, classical physics only explains matter and energy on a scale familiar to human experience, including the behavior of astronomical bodies such as the Moon.
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