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Decentralised Detection of Emergence in Complex Adaptive Systems

Published:06 April 2017Publication History
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Abstract

This article describes Decentralised Emergence Detection (DETect), a novel distributed algorithm that enables agents to collaboratively detect emergent events in Complex Adaptive Systems (CAS). Non-deterministic interactions between agents in CAS can give rise to emergent behaviour or properties at the system level. The nature, timing, and consequence of emergence is unpredictable and may be harmful to the system or individual agents. DETect relies on the feedback that occurs from the system level (macro) to the agent level (micro) when emergence occurs. This feedback constrains agents at the micro level and results in changes occurring in the relationship between an agent and its environment. DETect uses statistical methods to automatically select the properties of the agent and environment to monitor and tracks the relationship between these properties over time. When a significant change is detected, the algorithm uses distributed consensus to determine if a sufficient number of agents have simultaneously experienced a similar change. On agreement of emergence, DETect raises an event, which its agent or other interested observers can use to act appropriately. The approach is evaluated using a multi-agent case study.

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      • Published in

        cover image ACM Transactions on Autonomous and Adaptive Systems
        ACM Transactions on Autonomous and Adaptive Systems  Volume 12, Issue 1
        March 2017
        113 pages
        ISSN:1556-4665
        EISSN:1556-4703
        DOI:10.1145/3071074
        Issue’s Table of Contents

        Copyright © 2017 ACM

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        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 6 April 2017
        • Revised: 1 November 2016
        • Accepted: 1 November 2016
        • Received: 1 December 2015
        Published in taas Volume 12, Issue 1

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