A multi-agent system with distributed bayesian reasoning for network fault diagnosis

In this paper, an innovative approach to perform distributed Bayesian inference using a multi-agent architecture is presented. The final goal is dealing with uncertainty in network diagnosis, but the solution can be of applied in other fields. The validation testbed has been a P2P streaming video service. An assessment of the work is presented, in order to show its advantages when it is compared with traditional manual processes and other previous systems.

Behaviour Driven Development for Multi-Agent Systems

This paper presents a testing methodology to apply Behaviour Driven Development (BDD) techniques while developing Multi-Agent Systems (MAS), so called BEhavioural Agent Simple Testing (BEAST) methodology. It is supported by the developed open source framework (BEAST Tool) which automatically generates test cases skeletons from BDD scenarios specifications. The developed framework allows testing MASs based on JADE or JADEX platforms and offers a set of configurable Mock Agents which allow the execution of tests while the system is under development. BEAST tool has been validated in the development of a MAS for fault diagnosis in FTTH (Fiber To The Home) networks.

UER technique: conceptualisation for agent oriented development

The problem of conceptualisation is the first step towards the identication of the functional requirements of a system. This article proposes two extensions of well-known object oriented techniques: UER (User-Environment-Responsibility) technique and enhanced CRC (Class-ResponsibilityCollaboration) cards. UER technique consists of (a) looking for the users of systems and describing the ways the system is used; (b) looking for the objects of the environment and describing the possible interactions; and (c) looking for the general requirements or goals of the system, the actions that it should carry out without explicit interaction. The enhanced CRC cards together with the internal use cases technique is used for dening collaborations between agents. These techniques can be easily integrated in UML (Unied Modelling Language) [2], dening the new notation symbols as stereotypes.

Agent Architecture Modelling at the Knowledge Level

The definition of an agent architecture at the knowledge level makes emphasis on the knowledge role played by the data interchanged between the agent components and makes explicit this data interchange this makes easier the reuse of these knowledge structures independently of the implementation This article defines a generic task model of an agent architecture and refines some of these tasks using the interference diagrams. Finally, a operationalisation of this conceptual model using the rule-oriented language Jess is shown. knowledge level,