Knowledge Modelling in Multiagent Systems: The Case of the Management of a National Network

Molina, Martín; Ossowskí, Sascha

doi:10.1007/3-540-48888-x_48

Cited by 3 publications

(2 citation statements)

References 11 publications

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“…Because speed is crucial for ExperNet's operation, we chose the establish and refine method 7 for diagnosis, so that we could quickly focus on network malfunctions with- out using deep models of network components. 4 This method uses an abstract reasoning pattern based on a heuristic search in a taxonomy of problem hypotheses; we adapted it to use three primitive inference steps:…”

Section: Local Problem Solvingmentioning

confidence: 99%

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ExperNet: an intelligent multiagent system for WAN management

Vlahavas

Bassitiades²,

Sakellariou

et al. 2002

IEEE Intell. Syst.

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Managing a large data network, such as a national WAN, is a complex and cumbersome task. Existing network management software cannot meet the requirements of the increasing size and complexity of today's TCP/IP-based networks, because, in most cases, it provides only simple monitoring tools. We can't fully exploit a WAN without user-friendly, intelligent, network management software enhanced with diagnostic and decision support services.Expert systems provide a feasible and elegant solution in this direction. Currently, the development of expert systems for WAN management is only at the research and experimental stages. Formalizing such a task is difficult because network state information is usually inadequate and incomplete, the scale of behavior characteristics is large, and the network environment continually evolves. Moreover, an efficient network-monitoring schema must exist. 1 The implementation of such functionality must be in accordance with existing network monitoring technology-namely, the Management Information Base (MIB) and the Simple Network Management Protocol (SNMP)-to control existing real-world network devices.Network management also requires coordination among operators of autonomous network nodes. To closely resemble network management's distributed nature, multiagent problem-solving techniques can help model the functionality of individual agents as well as the interactions taking place between them. 2 Multiagent technology is preferable to other distributed problem-solving techniques because it offers enhanced modularity, reactions to environmental changes, and reusability. This article presents the architecture, implementation, operation, and evaluation of the ExperNet system, a multiagent expert system that we developed for Ukraine's national TCP/IP WAN under the framework of a joint EUfunded project. ExperNet helps network operators manage a WAN with its• monitoring tools for capturing network state; • platform for logic-based applications; • efficient and extensible expert system shell; • fast heuristic detection, diagnosis, and repair of network failures; • cooperative problem-solving strategies; and • user-friendly Web-based interface.The system architecture ExperNet consists of a hierarchically structured architecture, with each level consisting of one or more management nodes (see Figure 1). Each node encapsulates one or more lower-level management subnodes and manages a network area, assisted by a

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Section: Local Problem Solvingmentioning

confidence: 99%

“…4 Conversations that cope with responsibility conflicts are simple because they just involve one interaction, transferring the responsibility for some task from sender to receiver. We have used three kinds of conversations of this type: diagnosis and repair delegation, repair delegation, and isolation delegation.…”

Section: Social Coordinationmentioning

confidence: 99%

ExperNet: an intelligent multiagent system for WAN management

Vlahavas

Bassitiades²,

Sakellariou

et al. 2002

IEEE Intell. Syst.

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A Multi-agent System for Emergency Decision Support

Molina

Blasco

2003

Lecture Notes in Computer Science

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Abstract. This paper describes the multi-agent organization of a computer system that was designed to assist operators in decision making in the presence of emergencies. The application was developed for the case of emergencies caused by river floods. It operates on real-time receiving data recorded by sensors (rainfall, water levels, flows, etc.) and applies multi-agent techniques to interpret the data, predict the future behavior and recommend control actions. The system includes an advanced knowledge based architecture with multiple symbolic representation with uncertainty models (bayesian networks). This system has been applied and validated at two particular sites in Spain (the Jucar basin and the South basin).

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