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Solving Distributed CSPs Using Dynamic, Partial Centralization without Explicit Constraint Passing

  • Roger Mailler
  • Jacob Graves
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7057)

Abstract

Dynamic, partial centralization has received a considerable amount of attention in the distributed problem solving community. As the name implies, this technique works by dynamically identifying portions of a shared problem to centralize in order to speed the problem solving process. Currently, a number of algorithms have been created which employ this simple, yet powerful technique to solve problems such as distributed constraint satisfaction (DCSP), distributed constraint optimization (DCOP), and distributed resource allocation.

In fact, one such algorithm, Asynchronous Partial Overlay (APO), was shown to outperform the Asynchronous Weak Commitment (AWC) protocol, which is one of the best known methods for solving DCSPs. One of the key differences between these algorithms is that APO uses explicit constraint passing. AWC, on the other hand, passed nogoods because it tries to provide security and privacy. Because of these differences in underlying assumptions, a number of researchers have criticized the comparison between these two protocols.

This paper attempts to resolve this disparity by introducing a new hybrid algorithm called Nogood-APO. Like AWC, this new algorithm uses nogood passing to provide security and privacy, but like APO uses dynamic partial centralization to speed the problem solving process. Like its parent algorithms, this new protocol is sound and complete and performs nearly as well as APO, while still outperforming AWC, on distributed 3-coloring problems. In addition, this paper shows that Nogood-APO provides more privacy to the agents than both APO and AWC on all but the sparsest problems. These findings demonstrate that a dynamic, partial centralization-based protocol can provide privacy and that even when operating with the same assumptions as AWC still solves problems in fewer cycles using less computation and communication.

Keywords

Information Gain Constraint Graph Sparse Problem Agent View Distribute Resource Allocation 
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 2012

Authors and Affiliations

  • Roger Mailler
    • 1
  • Jacob Graves
    • 1
  1. 1.Computational Neuroscience and Adaptive Systems LabUniversity of TulsaUSA

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