Agent-Based Modelling of Ecosystems for Sustainable Resource Management

Doran, Jim

doi:10.1007/3-540-47745-4_18

Cited by 24 publications

(18 citation statements)

References 12 publications

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“…Multi-agent systems have proven their reliability when being used in numerous areas like: (1) the road traffic control ( [4], [5]); (2) biologic phenomena simulation like the study of eco-systems [6] or the study of ant-colonies [7], for example; (3) social phenomena simulation like the study of consumer behaviors in a competitive market [8]; (4) industrial applications like the control of electrical power distribution systems, the negotiation of brands, etc. By its nature, multi-agent approach is well suited to control distributed systems.…”

Section: Adaptive and Autonomous Systemmentioning

confidence: 99%

Autonomous Network Equipments

Gaïti

Pujolle

Salaun

et al. 2006

Lecture Notes in Computer Science

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Abstract. IP networks are now well established. However, control, management and optimization schemes are provided in a static and basic way. Network control and management schemes using an autonomy based technology offer a new way to master quality of service, security and mobility management. This new paradigm allows a dynamic and intelligent control of the equipment in a local manner, a global network control in a cooperative manner, a more powerful network management, and a better guaranty of all vital functionalities like end to end quality of service and security. In this paper, we provide a way to implement such a paradigm through the use of the agent and multi agent concept. A testbed of an architecture based on autonomous network equipment has been developed. This autonomous architecture is able to optimize the quality of service through the networks.

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Section: Adaptive and Autonomous Systemmentioning

confidence: 99%

Autonomous Network Equipments

Gaïti

Pujolle

Salaun

et al. 2006

Lecture Notes in Computer Science

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“…Ferber [9] defines an agent as being an entity which: (1) can communicate directly with other agents, (2) possesses its own resources, (3) is capable of perceiving its environment (but to a limited extent), (4) has only a partial representation of its environment (and perhaps none at all), (5) has a behavior which tends towards satisfying its objectives, taking account of the resources and skills available to him and depending on its perception, its representation and the communications it receives. Multi-agent systems have been used in numerous areas like: (1) the road traffic control ( [4], [14]); (2) biologic phenomena simulation like the study of eco-systems [7] or that of ant-colonies [8], for example; (3) social phenomena simulation like the study of consumer behaviors in a competitive market [5]; (4) industrial applications like the control of electrical power distribution systems, the negotiation of brands, etc. ; (5) etc.…”

Section: Multi-agent Approachmentioning

confidence: 99%

“…), by adaptively monitoring the network elements, the traffic nature and volume, and the user profile and his (her) habits. Agents can be reactive, cognitive, hybrid or adaptive [3], [7], [22]. Reactive agents are suitable for situations where we need less treatment and faster responses (actions).…”

Section: Multi-agent Approachmentioning

confidence: 99%

On Using Multi-agent Systems in End to End Adaptive Monitoring

Merghem

Gaïti

Pujolle

2003

Management of Multimedia Networks and Services

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Abstract. The complexity (in terms of services and multimedia streaming) and dynamicity of telecommunication networks are continually growing, making network management and control more and more difficult. Such a management must be adaptive, dynamic and smart. In order to be sure of getting the best management mechanisms' choice, a monitoring operation becomes fundamental. This paper presents a new adaptive monitoring approach based on a multiagent system, taking into consideration the features of the networks namely flexibility, dynamicity, heterogeneity of users generated traffic, etc. A twolayers monitoring architecture is proposed in this paper. The first layer is responsible for dealing with monitoring of some local parameters, which are useful for the second layer. Indeed, this latter is adaptive, autonomous and able to make new decisions-about the data to monitor and the management mechanisms to activate-based on the current node's state.

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“…We have opted for adaptive ones because they support, by definition, our goals. Adaptive agents are indeed entities that change their behaviour in the light of changing circumstances; they can sense their environment and act upon what they sense, hence the term adaptive [2], [5], [7]. They can be therefore a good candidate to represent active entities, that change their behaviour following the network states.…”

Section: 2mentioning

confidence: 99%

“…In order to obtain an agent-based model, several steps have to be foHowed [5], (1) determine what we have to model, i.e. what are the entities we need to represent, (2) decide what must be active in a network: will it be nodes?…”

Section: Introductionmentioning

confidence: 99%

Active Network Modelling and Simulation: A Behavioural Approach

Gaïti¹,

Merghem²

2002

Smart Networks

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Network environments become more and more complex in terms of new services, quality of service to handle and user demand for increasing throughput. All these facts have to be performed in real time (or alm ost), and consequently, network management and control are difficult to realise in this continuously changing environment. The network is now unpredictable and there is areal lack in the modelisation and simulation tools to handle this dynamicity. In this context, agents and multi-agent systems seem to be good candidates to provide a way to model and to manage the network dynamicity. This is due to the fact that agents can perform tasks in an autonomous, distributed, adaptable and cooperative manner. Our goal here is to model an active network by using a multi-agent approach and more precisely a behaviour based agent approach. Instead ofrepresenting the actions ofthe network elements by complex algorithms, we rather describe them by their behaviours which are usually simple and familiar words, inspired in part from human behaviour. On this basis, we have made simulations of an active network environment, and implemented different combinations of four individual behaviours by using a multi-agent platform called Swarm. Results ofthese simulations are significant and show that we do not loose important packets (with priority) any more when we introduce a behaviour in the nodes.

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Agent-Based Modelling of Ecosystems for Sustainable Resource Management

Cited by 24 publications

References 12 publications

Autonomous Network Equipments

Autonomous Network Equipments

On Using Multi-agent Systems in End to End Adaptive Monitoring

Active Network Modelling and Simulation: A Behavioural Approach

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