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A comprehensive, self-contained introduction to constraint programming, in which programs are represented as relational equations between values and variables, and execution consists of assigning values to the variables that maintain the relational equations.
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Programming with constraints: an introduction
1998, MIT Press
Hardcover
in English
0262133415 9780262133418
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Book Details
Table of Contents
Table of Contents
Page vii
Preface
Page xi
Introduction
Page 1
Notes
Page 6
I.
Constraints
Page 9
1.
Constraints
Page 11
1.1.
Constraints and Valuations
Page 11
1.2.
Modelling with Constraints
Page 15
1.3.
Constraint Satisfaction
Page 18
1.4.
Tree Constraints
Page 22
1.5.
Other Constraint Domains
Page 27
1.6.
Properties of Constraint Solvers
Page 33
1.7.
(*) Determined Variables and Local Propagation
Page 35
1.8.
Summary
Page 41
1.9.
Exercises
Page 42
1.10.
Practical Exercises
Page 43
1.11.
Notes
Page 47
2.
Simplification, Optimization and Implication
Page 51
2.1.
Constraint Simplification
Page 51
2.2.
Projection
Page 53
2.3.
Constraint Simplifiers
Page 57
2.4.
Optimization
Page 61
2.5.
The Simplex Algorithm
Page 63
2.6.
(*) Canonical Form Simplifiers
Page 72
2.7.
(*) Implication and Equivalence
Page 75
2.8.
Summary
Page 78
2.9.
Exercises
Page 79
2.10.
Practical Exercises
Page 80
2.11.
Notes
Page 83
3.
Finite Constraint Domains
Page 85
3.1.
Constraint Satisfaction Problems
Page 86
3.2.
A Simple Backtracking Solver
Page 88
3.3.
Node and Arc Consistency
Page 91
3.4.
Bounds Consistency
Page 97
3.5.
Generalized Consistency
Page 109
3.6.
Optimization for Arithmetic CSPs
Page 114
3.7.
Summary
Page 120
3.8.
Exercises
Page 122
3.9.
Practical Exercises
Page 123
3.10.
Notes
Page 127
II.
Constraint Logic Programming
Page 131
4.
Constraint Logic Programs
Page 133
4.1.
User-Defined Constraints
Page 137
4.2.
Programming with Rules
Page 140
4.3.
Evaluation
Page 143
4.4.
Derivation Trees and Finite Failure
Page 146
4.5.
Goal Evaluation
Page 149
4.6.
Simplified Derivation Trees
Page 151
4.7.
The CLP Scheme
Page 153
4.8.
(*) Independence from Rule Ordering and Literal Selection
Page 158
4.9.
Summary
Page 158
4.10.
Exercises
Page 158
4.11.
Practical Exercises
Page 160
4.12.
Notes
Page 164
5.
Simple Modelling
Page 167
5.1.
Simple Modelling
Page 167
5.2.
Modelling Choice
Page 169
5.3.
Iteration
Page 174
5.4.
Optimization
Page 179
5.5.
Summary
Page 181
5.6.
Practical Exercises
Page 181
5.7.
Notes
Page 184
6.
Using Data Structures
Page 185
6.1.
Records
Page 186
6.2.
Lists
Page 188
6.3.
Association Lists
Page 193
6.4.
Binary Trees
Page 198
6.5.
Hierarchical Modelling
Page 201
6.6.
Tree Layout
Page 203
6.7.
Summary
Page 208
6.8.
Practical Exercises
Page 208
6.9.
Notes
Page 211
7.
Controlling Search
Page 213
7.1.
Estimating the Efficiency of a CLP Program
Page 213
7.2.
Controlling Search: An Example
Page 217
7.3.
Rule Ordering
Page 220
7.4.
Literal Ordering
Page 220
7.5.
Adding Redundant Constraints
Page 223
7.6.
Minimization
Page 227
7.7.
Identifying Deterministic Subgoals
Page 230
7.8.
An Extended Example: Bridge Building
Page 235
7.9.
Summary
Page 246
7.10.
Exercises
Page 247
7.11.
Practical Exercises
Page 248
7.12.
Notes
Page 250
8.
Modelling with Finite Domain Constraints
Page 251
8.1.
Domains and Labelling
Page 252
8.2.
Complex Constraints
Page 256
8.3.
Labelling
Page 258
8.4.
Different Problem Modellings
Page 266
8.5.
An Extended Example: Scheduling
Page 272
8.6.
(*) Arc Consistency
Page 281
8.7.
(*) Library Predicates
Page 284
8.8.
Summary
Page 285
8.9.
Practical Exercises
Page 286
8.10.
Notes
Page 291
9.
Advanced Programming Techniques
Page 293
9.1.
Extending the Constraint Solver
Page 293
9.2.
Combined Symbolic and Arithmetic Reasoning
Page 298
9.3.
Programming Optimization
Page 301
9.4.
Higher-order Predicates
Page 307
9.5.
Negation
Page 309
9.6.
CLP Languages with Dynamic Scheduling
Page 313
9.7.
(*) Meta Programming
Page 320
9.8.
(*) Library Predicates
Page 324
9.9.
Summary
Page 342
9.10.
Practical Exercises
Page 343
9.11.
Notes
Page 346
10.
CLP Systems
Page 349
10.1.
Simple Backtracking Goal Evaluation
Page 349
10.2.
Incremental Constraint Solving
Page 352
10.3.
Efficient Saving and Restoring of the Constraint Store
Page 358
10.4.
Implementing If-Then-Else, Once and Negation
Page 361
10.5.
Optimization
Page 366
10.6.
Other Incremental Constraint Solvers
Page 369
10.7.
(*) Incremental Real Arithmetic Solving
Page 376
10.8.
Summary
Page 385
10.9.
Exercises
Page 385
10.10.
Notes
Page 387
III.
Other Constraint Programming Languages
Page 389
11.
Constraint Databases
Page 391
11.1.
Modelling with Constraint Databases
Page 391
11.2.
Bottom Up Evaluation
Page 395
11.3.
Bottom-Up versus Top-Down
Page 405
11.4.
Mimicking Top-Down Evaluation Bottom-Up
Page 407
11.5.
(*) Improving Termination
Page 412
11.6.
(*) Relationship with Relational Databases
Page 416
11.7.
Summary
Page 421
11.8.
Exercises
Page 422
11.9.
Practical Exercises
Page 422
11.10.
Notes
Page 423
12.
Other Constraint Programming Languages
Page 425
12.1.
The CLP Paradigm
Page 425
12.2.
Concurrent Constraint Programming Languages
Page 427
12.3.
Constraint Handling Rules
Page 430
12.4.
Functional Languages
Page 433
12.5.
Term Rewriting
Page 436
12.6.
Imperative Programming Languages
Page 439
12.7.
Constraint Solving Toolkits
Page 440
12.8.
Mathematical Languages
Page 443
12.9.
Notes
Page 447
References
Page 449
Index
Page 459
Edition Notes
Includes bibliographical references and index.
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