Expert systems for configuration at Digital: XCON and beyond
Communications of the ACM
Configuring Large Systems Using Generative Constraint Satisfaction
IEEE Intelligent Systems
Asynchronous Search with Aggregations
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Consistency Maintenance for ABT
CP '01 Proceedings of the 7th International Conference on Principles and Practice of Constraint Programming
Distributed Dynamic Backtracking
CP '01 Proceedings of the 7th 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
Generative constraint-based configuration of large technical systems
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Conceptual modelling for configuration: A description logic-based approach
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
A classification and constraint-based framework for configuration
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
An overview of knowledge‐based configuration
AI Communications
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Today's configuration systems are centralized and do not allow manufacturers to collaborate online for offer-generation or sales-configuration activities. However, the integration of configurable products into the supply-chain of a business requires the cooperation of the various manufacturers' configuration systems to jointly offer valuable solutions to customers. As a consequence, there is a need for methods that enable independent specialized agents to compute such configurations. Several approaches to centralizedconfiguration are based on constraint satisfaction problem (CSP) solving. Most of them extend traditional CSP approaches in order to comply to the specific expressivity and dynamism requirements of configuration and similar synthesis tasks.The distributed generative CSP (DisGCSP) framework proposed here builds on a CSP formalism that encompasses the generativeaspect of variable creation and extensible domains of problem variables. It also builds on the distributed CSP (DisCSP) framework, supporting configuration tasks where knowledge is distributed over a set of agents. Notably, the notions of constraint and nogood are further generalized, adding an additional level of abstraction and extending inferences to types of variables. An example application of the new framework describes modifications to the ABT algorithms and furthermore our evaluation indicates that the DisGCSP framework is superior to classic DisCSP for typical configuration task problem encoding.