Scheduling jobs with fixed start and end times
Discrete Applied Mathematics
An evolution of interval graphs
Discrete Mathematics
Interchangeability preprocessing can improve forward checking search
ECAI '92 Proceedings of the 10th European conference on Artificial intelligence
On the computation of local interchangeability in discrete constraint satisfaction problems
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
SARA '02 Proceedings of the 4th International Symposium on Abstraction, Reformulation, and Approximation
Dynamic Bundling: Less Effort for More Solutions
Proceedings of the 5th International Symposium on Abstraction, Reformulation and Approximation
Exploiting Interchangeabilities for Case Adaptation
ICCBR '01 Proceedings of the 4th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Constrained-Based Problem Decomposition for a Key Configuration Problem
CP '98 Proceedings of the 4th International Conference on Principles and Practice of Constraint Programming
Soft interchangeability for case adaptation
ICCBR'03 Proceedings of the 5th international conference on Case-based reasoning: Research and Development
Relaxation of qualitative constraint networks
SARA'07 Proceedings of the 7th International conference on Abstraction, reformulation, and approximation
WCSP integration of soft neighborhood substitutability
CP'12 Proceedings of the 18th international conference on Principles and Practice of Constraint Programming
Interchangeability with thresholds and degradation factors for Soft CSPs
Annals of Mathematics and Artificial Intelligence
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Resource allocation is a difficult constraint satisfaction problem that has many practical applications. Fully automatic systems are often rejected by the ultimate users because, in many real-world environments, constraints cannot be formalized completely. On the other hand, humans are overwhelmed by the complexity of their task. We present a new way of solving the resource allocation, where a computer builds dynamic abstractions that simplify problem solving to the point that the user can intervene in the solution of the problem. These abstractions are based on the concept of interchangeability introduced by Freuder. In this paper, we describe a heuristic for decomposing a resource allocation problem into abstractions that reflect interchangeable sets of tasks or resources. We assess the "quality" of the discovered neighborhood interchangeable sets by comparing them to the ones obtained by the exact algorithm described by Freuder, both for data taken from a real-world application and for randomly generated problems.