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Evan A. Sultanik

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6 papers
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6

IJCAI Conference 2009 Conference Paper

  • Evan A. Sultanik
  • Robert N. Lass
  • William C. Regli

It is useful to impose organizational structure over multiagent coalitions. Hierarchies, for instance, allow for compartmentalization of tasks: if organized correctly, tasks in disjoint subtrees of the hierarchy may be performed in parallel. Given a notion of the way in which a group of agents need to interact, the Dynamic Distributed Multiagent Hierarchy Generation (DynDisMHG) problem is to determine the best hierarchy that might expedite the process of coordination. This paper introduces a distributed algorithm, called Mobed, for both constructing and maintaining organizational agent hierarchies, enabling exploitation of parallelism in distributed problem solving. The algorithm is proved correct and it is shown that individual additions of agents to the hierarchy will run in an amortized linear number of rounds. The hierarchies resulting after perturbations to the agent coalition have constantbounded edit distance, making Mobed very well suited to highly dynamic problems.

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IJCAI Conference 2007 Conference Paper

  • Evan A. Sultanik
  • Pragnesh Jay Modi
  • William C. Regli

This paper investigates how to represent and solve multiagent task scheduling as a Distributed Constraint Optimization Problem (DCOP). Recently multiagent researchers have adopted the C_TAEMS language as a standard for multiagent task scheduling. We contribute an automated mapping that transforms C_TAEMS into a DCOP. Further, we propose a set of representational compromises for C_TAEMS that allow existing distributed algorithms for DCOP to be immediately brought to bear on C_TAEMS problems. Next, we demonstrate a key advantage of a constraint based representation is the ability to leverage the representation to do efficient solving. We contribute a set of pre-processing algorithms that leverage existing constraint propagation techniques to do variable domain pruning on the DCOP. We show that these algorithms can result in 96% reduction in state space size for a given set of C_TAEMS problems. Finally, we demonstrate up to a 60% increase in the ability to optimally solve C_TAEMS problems in a reasonable amount of time and in a distributed manner as a result of applying our mapping and domain pruning algorithms.

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AAAI Conference 2005 Conference Paper

Stable Service Placement on Dynamic Peer-to-Peer Networks: A Heuristic for the Distributed k-Center Problem

  • Evan A. Sultanik

The proliferation of wireless networks has underscored the need for systems capable of coping with sporadic network connectivity. The restriction of communication to neighboring hosts makes determining the global state especially difficult, if not impractical. This paper addresses the problem of coordinating the positions of an arbitrary number of services, encapsulated by mobile agents, in a dynamic peer-topeer network. The agents’ collective goal is to minimize the distance between hosts and services, even if the topology is changing constantly. We propose a distributed algorithm to efficiently calculate the stationary distribution of the network. This can be used as a hill climbing heuristic for agents to find near-optimal locations at which to provide services. Finally, we show that the agent-based hill climbing approach is temporally-stable relative to the instantaneous optimum.

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