Distributed Constraint Programming with Agents

  • Carl Christian Rolf
  • Krzysztof Kuchcinski
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6943)


Many combinatorial optimization problems lend themselves to be modeled as distributed constraint optimization problems (DisCOP). Problems such as job shop scheduling have an intuitive matching between agents and machines. In distributed constraint problems, agents control variables and are connected via constraints. We have equipped these agents with a full constraint solver. This makes it possible to use global constraint and advanced search schemes.

By empowering the agents with their own solver, we overcome the low performance that often haunts distributed constraint satisfaction problems (DisCSP). By using global constraints, we achieve far greater pruning than traditional DisCSP models. Hence, we dramatically reduce communication between agents.

Our experiments show that both global constraints and advanced search schemes are necessary to optimize job shop schedules using DisCSP.


Constraint Programming Precedence Constraint Constraint Satisfaction Problem Global Constraint Advanced Search 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Carl Christian Rolf
    • 1
  • Krzysztof Kuchcinski
    • 1
  1. 1.Department of Computer ScienceLund UniversitySweden

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