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Self-adapting agent organisations

Self-adapting agent organisations
Self-adapting agent organisations
Autonomic systems, capable of self-management, are being advocated as a solution to the problem of maintaining modern, large, complex computing systems. Given this, we believe self-organising multi-agent systems provide a convenient paradigm to develop these autonomic systems because such self-organising systems can arrange and re-arrange their structure autonomously, without any external control, in order to adapt to changing requirements and environmental conditions. Furthermore, such systems need to be decentralised, so that they are robust against failures; again, this characteristic fits with the multi-agent paradigm. With this motivation, this thesis explores the area of self-organisation in agent systems, and particularly focuses on the decentralised structural adaptation of agent organisations.

In more detail, self-organisation has been generated in agent systems using various approaches like stigmergy, reinforcement mechanisms, cooperative actions of agents and reward based mechanisms for selfish agents. However, none of these are directly applicable to agent organisations because they cannot be incorporated into deliberative agents working towards organisational goals. The few adaptation mechanisms that are applicable are either centralised or are based on restricted settings and also ignore the resources being used by the adaptation process. Here, we particularly focus on such problem solving agent organisations because they provide a suitable representation for autonomic systems. We investigate and develop mechanisms to incorporate decentralised structural adaptation in organisations to improve their performance.

More specifically still, we provide a generic framework for representing problem solving agent organisations. This serves as the platform on which we investigate approaches for structural adaptation. Following this, we demonstrate a robust, decentralised adaptation method that enables the agents to modify the organisational structure. As the method is based on self organisation principles, the agents use only their local views to change their structural relations to achieve a better allocation of tasks in the organisation. Particularly, 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 organisations using our method is close (70?90%) to that of an idealised centralised allocation method and is considerably better (10?45%) than the current state of the art decentralised approaches.
Kota, Ramachandra
a2b6c536-fa54-4d9e-8f3d-c3fb66f79b86
Kota, Ramachandra
a2b6c536-fa54-4d9e-8f3d-c3fb66f79b86
Jennings, Nicholas
ab3d94cc-247c-4545-9d1e-65873d6cdb30
Gibbins, Nicholas
98efd447-4aa7-411c-86d1-955a612eceac

Kota, Ramachandra (2009) Self-adapting agent organisations. University of Southampton, School of Electronics and Computer Science, Doctoral Thesis, 128pp.

Record type: Thesis (Doctoral)

Abstract

Autonomic systems, capable of self-management, are being advocated as a solution to the problem of maintaining modern, large, complex computing systems. Given this, we believe self-organising multi-agent systems provide a convenient paradigm to develop these autonomic systems because such self-organising systems can arrange and re-arrange their structure autonomously, without any external control, in order to adapt to changing requirements and environmental conditions. Furthermore, such systems need to be decentralised, so that they are robust against failures; again, this characteristic fits with the multi-agent paradigm. With this motivation, this thesis explores the area of self-organisation in agent systems, and particularly focuses on the decentralised structural adaptation of agent organisations.

In more detail, self-organisation has been generated in agent systems using various approaches like stigmergy, reinforcement mechanisms, cooperative actions of agents and reward based mechanisms for selfish agents. However, none of these are directly applicable to agent organisations because they cannot be incorporated into deliberative agents working towards organisational goals. The few adaptation mechanisms that are applicable are either centralised or are based on restricted settings and also ignore the resources being used by the adaptation process. Here, we particularly focus on such problem solving agent organisations because they provide a suitable representation for autonomic systems. We investigate and develop mechanisms to incorporate decentralised structural adaptation in organisations to improve their performance.

More specifically still, we provide a generic framework for representing problem solving agent organisations. This serves as the platform on which we investigate approaches for structural adaptation. Following this, we demonstrate a robust, decentralised adaptation method that enables the agents to modify the organisational structure. As the method is based on self organisation principles, the agents use only their local views to change their structural relations to achieve a better allocation of tasks in the organisation. Particularly, 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 organisations using our method is close (70?90%) to that of an idealised centralised allocation method and is considerably better (10?45%) than the current state of the art decentralised approaches.

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Published date: November 2009
Organisations: University of Southampton

Identifiers

Local EPrints ID: 72019
URI: http://eprints.soton.ac.uk/id/eprint/72019
PURE UUID: 3766b786-d17b-460a-9748-2dd72218c904
ORCID for Nicholas Gibbins: ORCID iD orcid.org/0000-0002-6140-9956

Catalogue record

Date deposited: 18 Jan 2010
Last modified: 11 Dec 2021 03:24

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Contributors

Author: Ramachandra Kota
Thesis advisor: Nicholas Jennings
Thesis advisor: Nicholas Gibbins ORCID iD

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