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pearl j heuristics intelligent search strategies 1984 fix

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Textbook in PDF format The study of heuristics draws its inspiration from the ever-amazing observation of how much people can accomplish with that simplistic, unreliable information source known as intuition. We drive our cars with hardly any thought of how they function and only a vague mental picture of the road conditions ahead. We write complex computer programs while attending to only a fraction of the possibilities and interactions that may take place in the actual execution of these programs. Even more surprisingly, we maneuver our way successfully in intricate social situations having only a guesswork expectation of the behavior of other persons around and even less certainty of their expectations of us. Yet, when these expectations fail we are able to master the great power of humor and recover gracefully. In more precise terms, heuristics stand for strategies using readily accessible though loosely applicable information to control problem-solving processes in human beings and machine. This book presents an analysis of the nature and the power of typical heuristic methods, primarily those used in artificial intelligence (AI) and operations research (OR) to solve problems of search, reasoning, planning and optimization on digital machines. Problem-Solving Strategies and the Nature of Heuristic Information Heuristics and Problem Representations Typical Uses of Heuristics in Problem Solving The 8-Queens Problem The 8-Puzzle The Road Map Problem The Traveling Salesman Problem (TSP) The Counterfeit Coin Problem Search Spaces and Problem Representations Optimizing, Satisficing, and Semi-Optimizing Tasks Systematic Search and the Split-and-Prune Paradigm State-Space Representation Problem-Reduction Representations and AND/OR Graphs Selecting a Representation Bibliographical and Historical Remarks Exercises Basic Heuristic-Search Procedures Hill-Climbing: An Irrevocable Strategy Uninformed Systematic Search: Tentative Control Strategies Depth-First and Backtracking: LIFO Search Strategies Breadth-First: A FIFO Search Strategy Uninformed Search of AND/OR Graphs Informed, Best-First Search: A Way of Using Heuristic Information A Basic Best-First (BF) Strategy for State-Space Search A General Best-First Strategy for AND/OR Graphs (GBF) Specialized Best-First Algorithms: Z*, A*, AO,and AO* Why Restrict the Evaluation Functions? Recursive Weight Functions Identifying G0, The Most Promising Solution-Base Graph Specialized Best-First Strategies Hybrid Strategies BF-BT Combinations Introducing Irrevocable Decisions Bibliographical and Historical Remarks Exercises Formal Properties of Heuristic Methods A* —Optimal Search for an Optimal Solution Properties of f* Termination and Completeness Admissibility —A Guarantee for an Optimal Solution Comparing the Pruning Power of Several Heuristics Monotone (Consistent) Heuristics Relaxing the Optimality Requirement Adjusting the Weights of g and h Two e-Admissible Speedup Versions of A* R6* — A Limited Risk Algorithm Using Information about the Uncertainty of h 90 / 3.2.4 R*( — A Speedup Version of /?6* Some Extensions to Nonadditive Evaluation Functions (BF* and GBF*) Notation and Preliminaries Algorithmic Properties of Best-First Search BF* Bibliographical and Historical Remarks Exercises Heuristics Viewed as Information Provided by Simplified Models The Use of Relaxed Models Where Do These Heuristics Come From? Consistency of Relaxation-Based Heuristics Overconstrained, Analogical, and Other Types of Auxiliary Models Mechanical Generation of Admissible Heuristics Systematic Relaxation 118 / 4.2.2 Can a Program Tell an Easy Problem When It Sees One? Summary Probability-Based Heuristics 1 Heuristics Based on the Most Likely Outcome Heuristics Based on Sampling ProbabilityBased Heuristics in the Service of Semi-Optimization Problems Bibliographical and Historical Remarks Exercises Performance Analysis of Heuristic Methods Abstract Models for Quantitative Performance Analysis Mathematical Performance Analysis, or Test Tubes versus Fruit Flies in the Design of Gothic Cathedrals Example 1: Finding a Shortest Path in a Regular Lattice with Air-Distance Heuristics Example 2: Finding a Shortest Path in a Road-Map with Randomly Distributed Cities Example 3: Searching for an Optimal Path in a Tree with Random Costs Notation and Preliminaries Summary of Results Branching Processes and the Proofs of Theorems 1-6 Conclusions Bibliographical and Historical Remarks Exercises Appendix 5-A: Basic Properties of Branching Processes Appendix 5-B: The Expected Size of an Extinct Family Appendix 5-C: Proof of Theorem 2 Complexity versus Precision of Admissible Heuristics Heuristics Viewed as Noisy Information Sources Simplified Models as Sources of Noisy Signals A Probabilistic Model for Performance Analysis A Formula for the Mean Complexity of A* Stochastic Dominance for Random Admissible Heuristics The Mean Complexity of A* under Distance-Dependent Errors The Average Complexity under Proportional Errors The Average Complexity under General Distance-Dependent Errors Comparison to Backtracking and the Effect of Multiple Goals The Mean Complexity of Informed Backtracking The Effect of Multiple Goals Exercises Searching with Nonadmissible Heuristics Conditions for Node Expansion When Is One Heuristic Better Than Another If Overestimations Are Possible? How to Improve a Given Heuristic The Effect of Weighting g and h How to Combine Information from Several Heuristic Sources When Is It Safe to Use f = h or, Who’s Afraid of w = 1? Exercises Appendix 7-A: Proof of Lemma 2 Appendix 7-B: Proof of Theorem 1 (The Pessimistic Substitution Principles) 2 Game-Playing Programs Strategies and Models for Game-Playing Programs Solving and Evaluating Games Game Trees and Game-Playing Strategies Bounded Look-Ahead and the Use of Evaluation Functions 2 MIN-MAX versus NEG-MAX Notations Basic Game-Searching Strategies Exhaustive Minimaxing and the Potential for Pruning The a-/3 Pruning Procedure: A Backtracking Strategy SSS* —A Best-First Search for an Optimal Playing Strategy SCOUT —A Cautious Test-Before-Evaluate Strategy A Standard Probabilistic Model for Studying the Performance of Game-Searching Strategies The Probability of Winning a Standard Game with Random Win Positions Game Trees with an Arbitrary Distribution of Terminal Values The Mean Complexity of Solving a Standard (d,6,P0)־game The Mean Complexity of Testing and Evaluating Multivalued Game Trees Recreational Diversions The Board-Splitting Game —A Physical Embodiment of the Standard Game Tree Other Applications of the Minimax Convergence Theorem Games as Mazes with Hidden Paths: A Useful Metaphor Bibliographical and Historical Remarks Exercises Performance Analysis for Game-Searching Strategies The Expected Performance of SCOUT Games with Continuous Terminal Values Games with Discrete Terminal Values The Expected Performance of a-(3 Historical Background An Integral Formula for Ia_0 (d,b) The Branching Factor of a-(3 and Its Optimality How Powerful Is the o!-/3 Pruning? The Expected Performance of SSS* A Necessary and Sufficient Condition for Node Examination The Probability of Examining a Terminal Node The Expected Number of Terminal Nodes Examined by SSS* The Branching Factor of SSS* Numerical Comparison of the Performances of qj-/3, SSS*, and SCOUT The Benefit of Successor Ordering Games with Random Number of Moves The Distribution of the Value of the Game Performance Analysis 322 / 9.5.3 Ordering Successors by Branching Degrees Bibliographical and Historical Remarks Exercises Appendix 9-A: Proof of Theorem 1 Appendix 9-B: Proof of Theorem 6 Decision Quality in Game Searching Error Propagation through Minimaxing Error-Propagation for Bi-Valued Estimates and Binary Trees Extensions to Multivalued Estimates and b-ary Trees Limit-Points of (ak,pk) The Effect of Searching Deeper When Is Look-Ahead Beneficial? Improved Visibility The Effect of Dependencies The Avoidance of Traps Playing to Win versus Playing Correctly Exercises Bibliography Glossary of Notation Author Index Subject Index

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