On Reformulation of Constraint Satisfaction Problems

Interchangeability Supports Abstraction and Reformulation for Multi-Dimensional Constraint Satisfaction

Interchangeability provides a principled approach to abstraction and reformulation of constraint satisfaction problems. Values are interchangeable if exchanging one for the other in any solution produces another solution. Abstracting a problem by simplifying the constraints can increase interchangeability. Multi-dimensional constraint satisfaction problems can provide natural opportunities for ...

for Multi - Dimensional Constraint Satisfaction

Interchangeability provides a principled approach to abstraction and reformulation of constraint satisfaction problems. Values are interchangeable if exchanging one for the other in any solution produces another solution. Abstracting a problem by simplifying the constraints can increase interchangeability. Multi-dimensional constraint satisfaction problems can provide natural opportunities for ...

Automatable High-Level Integration of Constraint Programs

We propose a reformulation algorithm as well as a set of re-formulation rules for models of constraint satisfaction problems written in our high-level constraint programming language esra, which is more expressive than opl and is compiled into opl. For the class of mapping problems, the reformulation algorithm achieves models that integrate a constraint programming formulation and an integer pr...

Greater Efficiency for Conditional Constraint Satisfaction

A conditional constraint satisfaction problem (CCSP) extends a standard constraint satisfaction problem (CPS) with a conditionbased component that controls what variables participate in problem solutions. CCSPs adequately represent configuration and design problems in which selected subsets of variables, rather than the entire variable set, are relevant to final solutions. The only algorithm th...

High-Level Modelling and Reformulation of Constraint Satisfaction Problems

Reformulating Constraint Models for Classical Planning

Constraint satisfaction techniques are commonly used for solving scheduling problems, still they are rare in AI planning. Although there are several attempts to apply constraint satisfaction for solving AI planning problems, these techniques never became predominant in planning; and they never reached the success of, for example, SATbased planners. In this paper we argue that existing constrain...