Álvaro Carrera

Beast methodology: An agile testing methodology for multi-agent systems based on behaviour driven development

This paper presents a testing methodology to apply Behaviour Driven Development (BDD) techniques while developing Multi-Agent Systems (MASs), termed BEhavioural Agent Simple Testing (BEAST) Methodology. This methodology is supported by the open source framework (BEAST Tool) which automatically generates test cases skeletons from BDD scenarios specifications. The developed framework allows the testing of MASs based on JADE or JADEX platforms. In addition, this framework offers a set of configurable Mock Agents with the aim of being able to execute tests while the MAS is under development. The BEAST Methodology presents transparent traceability from user requirements to test cases. Thus, the stakeholders can be aware of the project status. The methodology and the associated tool have been validated in the development of a MAS for fault diagnosis in FTTH (Fiber To The Home) networks. The results have been measured in quantifiable way obtaining a reduction of the tests implementation time.

A real-life application of multi-agent systems for fault diagnosis in the provision of an Internet business service

Given that telecommunications networks are constantly growing in complexity and heterogeneity, management systems have to work with incomplete data, handle uncertain situations and deal with dynamic environments. In addition, the high competitiveness in the telecommunications market requires cost cutting and customer retention by providing reliable systems. Thus, improving fault diagnosis systems and reducing the mean time to repair with automatic systems is an important area of research for telecommunications companies. This paper presents a Fault Diagnosis Multi-Agent System (MAS) applied for the management of a business service of Telefonica Czech Republic. The proposed MAS is based on an extended Belief-Desire-Intention (BDI) model that combines heterogeneous reasoning processes, ontology-based reasoning and Bayesian reasoning. This hybrid diagnostic technique is described in detail in the paper. The system has been evaluated with data collected during one and a half years of system operation on a live network. The main benefits of the system have been a significant reduction in both the average incident solution time and the mean diagnosis time.

A systematic review of argumentation techniques for multi-agent systems research

The ability to build arguments that express thoughts is crucial for intelligent interactions among human beings. Thus, argumentation techniques have been applied for years in fields, such as rhetoric or artificial intelligence. More specifically, the agents paradigm fits into the use of these types of techniques because agents shape a society in which they interact to make arrangements or to decide future actions. Those interactions can be modelled using argumentation techniques. Therefore, the application of those techniques in multi-agent systems is an interesting research field. However, no systematic review has been conducted previously, to the best of the authors’ knowledge, to provide an overview of argumentation techniques for multi-agent systems. This paper presents a systematic review of argumentation techniques for multi-agent systems research. The period of time that is included in this review is from 1998 to 2014. The objective of this review is to obtain an overview of the existing approaches and to study their impact on research and practice. The research method has been defined to identify relevant studies based on a predefined search strategy, and it is clearly defined to facilitate the reading of this paper. All of the included studies in this review have been analysed from two different points of view: the Application view and the Multi-Agent System view. A comprehensive analysis of the extracted data is provided in the paper, which is based on a set of research questions that are defined. The results of this review reveal suggestions for further research and practice. The argumentation technology is actually in a phase of internal enhancement and exploration. Moreover, the research interest in this topic has increased in the last years. Furthermore, several interesting findings are presented in the paper.

Multi-agent Architecture for Heterogeneous Reasoning under Uncertainty Combining MSBN and Ontologies in Distributed Network Diagnosis

This article proposes a MAS architecture for network diagnosis under uncertainty. Network diagnosis is divided into two inference processes: hypothesis generation and hypothesis confirmation. The first process is distributed among several agents based on a MSBN, while the second one is carried out by agents using semantic reasoning. A diagnosis ontology has been defined in order to combine both inference processes. To drive the deliberation process, dynamic data about the influence of observations are taken during diagnosis process. In order to achieve quick and reliable diagnoses, this influence is used to choose the best action to perform. This approach has been evaluated in a P2P video streaming scenario. Computational and time improvements are highlight as conclusions.

A Participatory Agent-Based Simulation for Indoor Evacuation Supported by Google Glass

Indoor evacuation systems are needed for rescue and safety management. One of the challenges is to provide users with personalized evacuation routes in real time. To this end, this project aims at exploring the possibilities of Google Glass technology for participatory multiagent indoor evacuation simulations. Participatory multiagent simulation combines scenario-guided agents and humans equipped with Google Glass that coexist in a shared virtual space and jointly perform simulations. The paper proposes an architecture for participatory multiagent simulation in order to combine devices (Google Glass and/or smartphones) with an agent-based social simulator and indoor tracking services.

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.

A Cognitive Assistant for learning Java featuring Social Dialogue

Abstract The application of natural language to improve the interaction of human users with information systems is a growing trend in the recent years. Advances in cognitive computing enable a new way of interaction that accelerates insight from existing information sources. In this paper, we propose a modular cognitive agent architecture for question answering featuring social dialogue improved for a specific knowledge domain. The proposed system has been implemented as a personal agent to assist students learning Java programming language. The developed prototype has been evaluated to analyze how users perceive the interaction with the system. We claim that including social dialogue in QA systems increases users satisfaction and makes them easily engage with the system. Finally, we present the evaluation results that support our hypotheses.

Towards fault diagnosis based on agent technology for wireless sensor networks

Fault Diagnosis is an essential management task for any telecommunication network and it is even more crucial for Wireless Sensor Networks due to their dynamic nature. Based on Agent Technology, this paper presents an architecture that combines different network and diagnosis models to carry out a Fault Diagnosis process: a Causal Model to relate fault root causes with their symptoms and a Structural Model to define the network and its properties. The proposed approach has been evaluated in a simulation environment with emulated MICAz devices in a motion detection scenario.

Distributed Fault Diagnosis Using Bayesian Reasoning in MAGNETO

Many of the emerging telecom services make use of Outer Edge Networks, in particular Home Area Networks. The configuration and maintenance of such services may not be under full control of the telecom operator which still needs to guarantee the service quality experienced by the consumer. Diagnosing service faults in these scenarios becomes especially difficult since there may be not full visibility between different domains. This paper describes the fault diagnosis solution developed in the MAGNETO project, based on the application of Bayesian Inference to deal with the uncertainty. It also takes advantage of a distributed framework to deploy diagnosis components in the different domains and network elements involved, spanning both the telecom operator and the Outer Edge networks. In addition, MAGNETO features self-learning capabilities to automatically improve diagnosis knowledge over time and a partition mechanism that allows breaking down the overall diagnosis knowledge into smaller subsets. The MAGNETO solution has been prototyped and adapted to a particular outer edge scenario, and has been further validated on a real testbed. Evaluation of the results shows the potential of our approach to deal with fault management of outer edge networks.

Improving diagnosis agents with hybrid hypotheses confirmation reasoning techniques

This article proposes a (MAS) architecture for network diagnosis under uncertainty. Network diagnosis is divided into two inference processes: hypotheses generation and hypotheses confirmation. The first process is distributed among several agents based on a (MSBN), while the second one is carried out by agents using semantic reasoning. A diagnosis ontology has been defined in order to combine both reasoning processes. To drive the deliberation process, the strength of influence obtained from (CDF) method is used during diagnosis process. In order to achieve quick and reliable diagnoses, this influence is used to choose the best action to perform. This approach has been evaluated in a P2P video streaming scenario. Computational and time improvements are highlighted as conclusions.