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Robust Regulation Adaptation in Multi-Agent Systems

Published: 01 September 2013 Publication History

Abstract

Adaptive organisation-centred multi-agent systems can dynamically modify their organisational components to better accomplish their goals. Our research line proposes an abstract distributed architecture (2-LAMA) to endow an organisation with adaptation capabilities. This article focuses on regulation-adaptation based on a machine learning approach, in which adaptation is learned by applying a tailored case-based reasoning method. We evaluate the robustness of the system when it is populated by non compliant agents. The evaluation is performed in a peer-to-peer sharing network scenario. Results show that our proposal significantly improves system performance and can cope with regulation violators without incorporating any specific regulation-compliance enforcement mechanisms.

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Published In

cover image ACM Transactions on Autonomous and Adaptive Systems
ACM Transactions on Autonomous and Adaptive Systems  Volume 8, Issue 3
September 2013
110 pages
ISSN:1556-4665
EISSN:1556-4703
DOI:10.1145/2518017
Issue’s Table of Contents
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 01 September 2013
Accepted: 01 May 2013
Revised: 01 April 2013
Received: 01 May 2012
Published in TAAS Volume 8, Issue 3

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Author Tags

  1. Machine learning
  2. adaptation
  3. case-based reasoning
  4. organisation-centred MAS
  5. regulation

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  • (2023)A multi-scenario approach to continuously learn and understand norm violationsAutonomous Agents and Multi-Agent Systems10.1007/s10458-023-09619-437:2Online publication date: 16-Aug-2023
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