This paper presents the past and present efforts of developing real-life applications of argumentation with the Gorgias preference-based structured argumentation framework of Logic Programming with Priorities . Since its free availability on the web in 2003, the Gorgias system has been used by different groups in a variety of real-life applications in areas such as medical support, network security, business computing, ambient intelligence and, recently, in the area of cognitive personal assistants. We briefly review the Gorgias framework and its past applications and present an emerging general methodological approach for developing “decision making” applications of argumentation. This approach allows the development of real world applications directly from their high-level requirements expressed in the language of the application without the need for familiarity with the underlying technical details of argumentation. A new tool, called Gorgias-B , supports this high-level development of applications and automatically generates the underlying argumentation theory in Gorgias code. The paper also reports on ongoing real-life applications in two domains: an eye-clinic assistant for first level support, and systems for patient data access and data sharing agreements according to the relevant legislation and contracts or policies of the several stakeholders involved. The proposed approach is quite general for this type of applications of argumentation and, thus, it can be used with any preference-based argumentation framework.

In this paper we present the Gaia2JADE process concerning how one can implement a multi-agent system with the JADE framework using the Gaia methodology for analysis and design purposes. This process is particularly dedicated to the conversion of Gaia models to JADE code. It is described using the Software Process Engineering Metamodel (SPEM) and extends the one proposed by FIPA for describing the Gaia modeling process. Thus, it proposes to potential MAS developers a process that covers the full software development lifecycle. This work is based on the experience we have acquired by applying this process for implementing a real-word multi-agent system conceived for providing e-services to mobile users. With this paper, we share this experience with future multi-agent systems (MAS) developers, who would like to follow this process, taking into account several technical issues that emerged during the implementation phase, helping them to easily model and implement their systems.

A multiagent system uses argumentation-based interaction in an ambient-intelligence context to provide services for people with different combinations of impairments. This paper focuses on ambient intelligence system of agents for knowledge-based and integrated services for mobility-impaired users integrated projectpsilas (ASK-ITIP) furthered the challenge by aiming to support users having different types and combinations of impairments. ASK-ITIP use of argumentation to model a distributed decision-making process for a coalition of assistant agents, each an expert on a different impairment. When a user suffers from a combination of impairments, these agents engage in an argumentation-based dialogue to agree on the user's needs. We found that applying argumentation was natural in this context because, generally speaking, we can abstractly define argumentation as the principled interaction of different, potentially conflicting arguments to obtain a consistent conclusion. Moreover, argumentation-based interaction is combined with a standardized interaction type based on the foundation for intelligent physical agents interaction protocol.

This article shows how to model and implement an ambient intelligence (AmI) system using agent technology. The HERA project, undertaken by a consortium with members from academia as well as industry, applied the agent systems engineering methodology (ASEME), an agent-oriented software engineering approach, to develop a real-world system for the ambient assisted living application domain. This article focuses on the software architecture, along with the development method and validation results. The obtained results demonstrate the added value of agent technology use, along with how ASEME can be applied for modeling a real-world ambient intelligence system.