Constraint satisfaction problem

Constraint satisfaction problems (CSPs) are mathematical questions defined as the set of objects whose state must satisfy a number of constraints or/ limitations. CSPs represent a entities in a problem as a homogeneous collection of finite constraints over variables, which is solved by constraint satisfaction methods. CSPs are the subject of research in both artificial intelligence and operations research, since the regularity in their formulation provides a common basis to analyze and solve problems of many seemingly unrelated families. CSPs often exhibit high complexity, requiring a combination of heuristics and combinatorial search methods to be solved in a reasonable time. Constraint programming (CP) is the field of research that specifically focuses on tackling these kinds of problems

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enConstraint satisfaction problems (CSPs) are mathematical questions defined as the set of objects whose state must satisfy a number of constraints or/ limitations. CSPs represent a entities in a problem as a homogeneous collection of finite constraints over variables, which is solved by constraint satisfaction methods. CSPs are the subject of research in both artificial intelligence and operations research, since the regularity in their formulation provides a common basis to analyze and solve problems of many seemingly unrelated families. CSPs often exhibit high complexity, requiring a combination of heuristics and combinatorial search methods to be solved in a reasonable time. Constraint programming (CP) is the field of research that specifically focuses on tackling these kinds of problems
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Communicating sequential processes
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enConstraint satisfaction problems (CSPs) are mathematical questions defined as the set of objects whose state must satisfy a number of constraints or/ limitations. CSPs represent a entities in a problem as a homogeneous collection of finite constraints over variables, which is solved by constraint satisfaction methods. CSPs are the subject of research in both artificial intelligence and operations research, since the regularity in their formulation provides a common basis to analyze and solve problems of many seemingly unrelated families. CSPs often exhibit high complexity, requiring a combination of heuristics and combinatorial search methods to be solved in a reasonable time. Constraint programming (CP) is the field of research that specifically focuses on tackling these kinds of problems. Additionally, Boolean satisfiability problem (SAT), the satisfiability modulo theories (SMT), mixed integer programming (MIP) and answer set programming (ASP) are all fields of research focusing on the resolution of particular forms of the constraint satisfaction problem. Examples of problems that can be modeled as a constraint satisfaction problem include: * Type inference * Eight queens puzzle * Map coloring problem * Maximum cut problem * Sudoku, Crosswords, Futoshiki, Kakuro (Cross Sums), Numbrix, Hidato and many other logic puzzles These are often provided with tutorials of CP, ASP, Boolean SAT and SMT solvers. In the general case, constraint problems can be much harder, and may not be expressible in some of these simpler systems. "Real life" examples include automated planning, lexical disambiguation, musicology, product configuration and resource allocation. The existence of a solution to a CSP can be viewed as a decision problem. This can be decided by finding a solution, or failing to find a solution after exhaustive search (stochastic algorithms typically never reach an exhaustive conclusion, while directed searches often do, on sufficiently small problems). In some cases the CSP might be known to have solutions beforehand, through some other mathematical inference process.
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Constraint satisfaction problem
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enConstraint satisfaction problem
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AC-3 algorithm
Answer set programming
Arc consistency
Artificial intelligence
Automated planning
Backjumping
Backmarking
Backtracking
Boolean logic
Boolean operator (Boolean algebra)
Boolean satisfiability problem
Category:Constraint programming
Combinatorial search
Complexity of constraint satisfaction
Computational complexity theory
Configure, price and quote
Conjunctive query
Constrained optimization
Constraint (mathematics)
Constraint composite graph
Constraint learning
Constraint programming
Constraint propagation
Constraint satisfaction
Crossword
Decision problem
Declarative programming
Dichotomy
Distributed constraint optimization
Eight queens puzzle
Finite model theory
FP (complexity)
Futoshiki
Fuzzy logic
Graph coloring
Graph homomorphism
Heuristics
Hidato
Hybrid algorithm (constraint satisfaction)
Hyper-arc consistency
Hypergraph
Kakuro
Ladner's theorem
Lexical disambiguation
Limit (mathematics)
Linear programming
Local consistency
Local search (optimization)
Logic puzzle
Look-ahead (backtracking)
Maximum cut
Min-conflicts algorithm
Mixed integer programming
Musicology
NP (complexity)
NP-complete
NP-intermediate
Numbrix
Operations research
P (complexity)
Path consistency
Preference-based planning
P versus NP problem
Recursion
Relation (mathematics)
Resource allocation
Satisfiability modulo theories
Schaefer's dichotomy theorem
Search algorithm
Sharp-P
Sharp-P-complete
State (computer science)
Sudoku
Treewidth
Type inference
Unique games conjecture
Variable (mathematics)
Very large-scale neighborhood search
Weighted constraint satisfaction problem
SameAs
BF4B
Constraint-Satisfaction-Problem
m.01f0mv
Problema da satisfação de restrições
Problema de satisfacción de restricciones
Problema di soddisfacimento di vincoli
Problème de satisfaction de contraintes
Q1128326
Задача виконання обмежень
Удовлетворение ограничений
בעיית סיפוק אילוצים
مسئله ارضای محدودیت
बाधा संतुष्टि की समस्या
制約充足問題
約束滿足問題
제약 충족 문제
Subject
Category:Constraint programming
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