Add to ORKGIn general, Constraint Optimization Problems (COP) are NP-hard. Using variable elimination techniques [5, 13] COPs can be solved with computation that is exponential only in the induced-width of the constraint graph (given some order on the nodes), i.e. smaller than n. Orders on nodes allowing for some parallelism are o#ered by Depth First Search (DFS) trees of the constraint graph [3, 13]
Soft neighborhood substitutability (SNS) is a powerful technique to automatically detect and prune d...
Many fundamental tasks in artificial intelligence and in combinatorial optimization can be formulate...
In this paper, we are interested in enumerative resolution methods for combinatorial optimiza-tion (...
To solve combinatorial problems, Constraint Programming builds high-level models that expose much of...
There are two main solving schemas for constraint satisfaction and optimization problems: i) search,...
Combinatorial optimization problems require selecting the best solution from a discrete (albeit ofte...
A general-purpose constraint satisfaction algorithm has been developed as part of the FLITE system f...
Many different multi-agent problems, such as distributed scheduling, can be formalized as distribute...
Tree projections provide a unifying framework to deal with most structural decomposition methods of ...
Constraint programming is a paradigm for solving combinatorial problems by checking whether constrai...
A combinatorial problem is the problem of finding an object with some desired property among a finit...
Abstract. There are two main solving schemas for constraint satisfaction and optimization problems: ...
This thesis presents several techniques that advance search-based algorithms for solving Constraint...
This paper deals with the combinatorial search problem of f inding values for a set of variables sub...
The last two decades have seen extraordinary advances in industrial applications of constraint satis...
Soft neighborhood substitutability (SNS) is a powerful technique to automatically detect and prune d...
Many fundamental tasks in artificial intelligence and in combinatorial optimization can be formulate...
In this paper, we are interested in enumerative resolution methods for combinatorial optimiza-tion (...
To solve combinatorial problems, Constraint Programming builds high-level models that expose much of...
There are two main solving schemas for constraint satisfaction and optimization problems: i) search,...
Combinatorial optimization problems require selecting the best solution from a discrete (albeit ofte...
A general-purpose constraint satisfaction algorithm has been developed as part of the FLITE system f...
Many different multi-agent problems, such as distributed scheduling, can be formalized as distribute...
Tree projections provide a unifying framework to deal with most structural decomposition methods of ...
Constraint programming is a paradigm for solving combinatorial problems by checking whether constrai...
A combinatorial problem is the problem of finding an object with some desired property among a finit...
Abstract. There are two main solving schemas for constraint satisfaction and optimization problems: ...
This thesis presents several techniques that advance search-based algorithms for solving Constraint...
This paper deals with the combinatorial search problem of f inding values for a set of variables sub...
The last two decades have seen extraordinary advances in industrial applications of constraint satis...
Soft neighborhood substitutability (SNS) is a powerful technique to automatically detect and prune d...
Many fundamental tasks in artificial intelligence and in combinatorial optimization can be formulate...
In this paper, we are interested in enumerative resolution methods for combinatorial optimiza-tion (...