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Adaptive and Non-adaptive Distribution Functions for DSA

  • Melanie Smith
  • Sandip Sen
  • Roger Mailler
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7057)

Abstract

Distributed hill-climbing algorithms are a powerful, practical technique for solving large Distributed Constraint Satisfaction Problems (DSCPs) such as distributed scheduling, resource allocation, and distributed optimization. Although incomplete, an ideal hill-climbing algorithm finds a solution that is very close to optimal while also minimizing the cost (i.e. the required bandwidth, processing cycles, etc.) of finding the solution. The Distributed Stochastic Algorithm (DSA) is a hill-climbing technique that works by having agents change their value with probability p when making that change will reduce the number of constraint violations. Traditionally, the value of p is constant, chosen by a developer at design time to be a value that works for the general case, meaning the algorithm does not change or learn over the time taken to find a solution. In this paper, we replace the constant value of p with different probability distribution functions in the context of solving graph-coloring problems to determine if DSA can be optimized when the probability values are agent-specific. We experiment with non-adaptive and adaptive distribution functions and evaluate our results based on the number of violations remaining in a solution and the total number of messages that were exchanged.

Keywords

Lateral Movement Constraint Violation Prob Array Neighboring Agent Distribute Constraint Satisfaction 
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

  • Melanie Smith
    • 1
  • Sandip Sen
    • 1
  • Roger Mailler
    • 1
  1. 1.Computational Neuroscience and Adaptive Systems LabUniversity of TulsaUSA

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