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A system of systems (SoS) represents a set of independent systems cooperating to achieve a common goal. Developing an SoS is complex and difficult, requiring significant efforts from system architects to coherently interface systems and make them interoperable. In this research, a core architecture to simplify the development of SoS supporting collaborative work is proposed. The approach is based on a new model called MBA: “Memory Broker Agent,” consisting of agents for encapsulating systems, brokers for handling requests for services and results, and memories for capitalizing and managing knowledge. This paper introduces the MBA model and the corresponding SoS core architecture. A prototype implementing the proposed approach has been built and applied to a case study in the software industry. Experiments were performed to show the interest of using a system of systems architecture and assessing the work to do for integrating a new system into an existing MBA SoS.

Epidemiologists constantly search for methodologies that help them better understand how diseases work. Populations urge these improvements to combat these diseases more effectively. The literature presents several authors defending the idea that epidemiologists should be able to develop causal models. In this area, the technique of structural equation models (SEM) has stood out in scientific research. Although SEM has been widely used in several research areas, it has been little explored by epidemiologists. Despite its evolution and efficiency, SEM has a gap in terms of discovering causalities. To fill this gap, this study developed an R package called BNPA, whose methodology joins the best of Bayesian network structural learning algorithms (BNSL) from data and path analysis (PA) a SEM subarea. The BNPA was built with pre-processing functions. Its main algorithm allows creating an input model to start the PA from a data set semi-automatically generating information to analyze the PA performance. An analysis of cardiovascular disease’s main predictors was performed using the BNPA with data from the Canadian Community Health Survey (CCHS). Multiple linear regression (MR) was used as a gold standard methodology; the results of BNPA matched 85% of MR results. In conclusion, BNPA is efficient and can benefit researchers, mainly novices, by enabling them to build PA models from data. Furthermore, statisticians and PA experts will have more time to support these researchers instead of creating an initial model.

Groupware and collaborative tools have been proposed to support cooperative work. However, they suffer from some rather severe limitations. Alternatively, multi-agent systems can be proposed to improve the situation. In the latter case, the user normally interacts with the system through a special agent called a personal assistant. In this paper, we describe the design of an ontology-based speech interface for personal assistants applied in the context of cooperative projects. We believe that this type of interface will improve the quality of assistance and increase collaboration between project members. We present the interface and its insertion into a multi-agent system designed for research and development projects. We describe the design of the interface, highlighting the role of ontologies for semantic interpretation. As a result of this conversational speech interface, we expect an increase in the quality of assistance and a reduction in the time needed to answer user’s requests.