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Decentralized approaches for self-adaptation in agent organizations

Published:04 May 2012Publication History
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Abstract

Self-organizing multi-agent systems provide a suitable paradigm for developing autonomic computing systems that manage themselves. Towards this goal, we demonstrate a robust, decentralized approach for structural adaptation in explicitly modeled problem solving agent organizations. Based on self-organization principles, our method enables the autonomous agents to modify their structural relations to achieve a better allocation of tasks in a simulated task-solving environment. Specifically, the agents reason about when and how to adapt using only their history of interactions as guidance. We empirically show that, in a wide range of closed, open, static, and dynamic scenarios, the performance of organizations using our method is close (70–90%) to that of an idealized centralized allocation method and is considerably better (10–60%) than the current state-of-the-art decentralized approaches.

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  1. Decentralized approaches for self-adaptation in agent organizations

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    Liz Sonenberg

    Self-adapting agent organizations have been promoted as a useful and suitable paradigm for open and dynamic agent systems. This paper argues for a flexible approach that provides a meta-reasoning component to agents using localized adaptation rather than a centralized mechanism. This novel approach is presented for meta-reasoning using social utility factors to help decide whether or not to adapt an agent organization. Organizational structure is defined in terms of agent relationships. Changing the organizational structure entails changing, dissolving, or adding localized individual agent relationships. In this paper, the organizational structure is implicitly represented in the individual relationships, not explicitly represented at an organizational level. Each agent may have superiors, subordinates, and peer agent relationships. Subordinates or peer agents provide services that can be allocated. There is no first-class agent organizational entity as such. To address the dynamic nature of organizations, the authors propose a weighted (decaying with time) factor to calculate the utility of the relationship. The authors present three algorithms to apply based on the type of organization. The fundamental algorithm, k-adapt, is extended for open organizations based on the WoLF principle: "Win or Learn Fast." In other words, if you are working well with spare capacity, don't change quickly; if not, restructure quickly. For dynamic organizations, the k-adapt algorithm is extended to include decay over time, so that more recent relationships have more importance. The paper includes a modest but useful review of related work. It will be of interest to both those who want a sense of why and how to study self-organization from a multiagent systems perspective, and those who are interested in the fine details of a carefully addressed approach in one particular setting. Online Computing Reviews Service

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    • Published in

      cover image ACM Transactions on Autonomous and Adaptive Systems
      ACM Transactions on Autonomous and Adaptive Systems  Volume 7, Issue 1
      Special section on formal methods in pervasive computing, pervasive adaptation, and self-adaptive systems: Models and algorithms
      April 2012
      365 pages
      ISSN:1556-4665
      EISSN:1556-4703
      DOI:10.1145/2168260
      Issue’s Table of Contents

      Copyright © 2012 ACM

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 4 May 2012
      • Accepted: 1 August 2010
      • Revised: 1 April 2010
      • Received: 1 June 2009
      Published in taas Volume 7, Issue 1

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