Tree clustering for constraint networks (research note)
Artificial Intelligence
Enhancement schemes for constraint processing: backjumping, learning, and cutset decomposition
Artificial Intelligence
Distributed snapshots: determining global states of distributed systems
ACM Transactions on Computer Systems (TOCS)
The Distributed Constraint Satisfaction Problem: Formalization and Algorithms
IEEE Transactions on Knowledge and Data Engineering
Product Configuration Frameworks-A Survey
IEEE Intelligent Systems
Configuring Large Systems Using Generative Constraint Satisfaction
IEEE Intelligent Systems
A Fixpoint Definition of Dynamic Constraint Satisfaction
CP '99 Proceedings of the 5th International Conference on Principles and Practice of Constraint Programming
Distributed Constraint Satisfaction Algorithm for Complex Local Problems
ICMAS '98 Proceedings of the 3rd International Conference on Multi Agent Systems
Interleaved Backtracking in Distributed Constraint Networks
ICTAI '01 Proceedings of the 13th IEEE International Conference on Tools with Artificial Intelligence
A complexity analysis of space-bounded learning algorithms for the constraint satisfaction problem
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Towards Distributed Configuration
KI '01 Proceedings of the Joint German/Austrian Conference on AI: Advances in Artificial Intelligence
A constraint-based approach for analysing financial market operations
Proceedings of the 14th International Conference on Computer Systems and Technologies
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Dynamic constraint satisfaction problem (DCSP) solving is one of the most important methods for solving various kinds of synthesis tasks, such as configuration. Todays configurators are standalone systems not supporting distributed configuration problem solving functionality. However, supply chain integration of configurable products requires the integration of configuration systems of different manufacturers, which jointly offer product solutions to their customers. As a consequence, we need problem solving methods that enable the computation of such configurations by several distributed configuration agents. Therefore, one possibility is the extension of the configuration problem from a dynamic constraint satisfaction representation to distributed dynamic constraint satisfaction (DDCSP). In this paper we will contribute to this challenge by formalizing the DDCSP and by presenting a complete and sound algorithm for solving distributed dynamic constraint satisfaction problems. This algorithm is based on asynchronous backtracking and enables strategies for exploiting conflicting requirements and design assumptions (i.e. learning additional constraints during search). The exploitation of these additional constraints is of particular interest for configuration because the generation and the exchange of conflicting design assumptions based on nogoods can be easily integrated in existing configuration systems.