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The 4th industrial revolution advent promotes the reorganization of the traditional hierarchical automation systems towards decentralized Cyber-Physical Systems (CPS). In this context, Artificial Intelligence (AI) can address the new requirements through the use of data-driven and distributed problem solving approaches, such those based on Machine-Learning and Multi-agent Systems. Although their promising perspectives to enable and manage intelligent Internet of Things environments, the traditional Cloud-based AI approaches are not suitable to handle many industrial scenarios, constrained by responsiveness and data sensitive. The solution lies in taking advantage of Edge and Fog computing to create a decentralized multi-level data analysis computing infrastructure that supports the development of industrial CPS. However, this is not a straightforward task, posing several challenges and demanding new approaches and technologies. In this context, this work discusses the distribution of intelligence along Cloud, Fog and Edge computing layers in industrial CPS, leveraging some research challenges and future directions.

Resumo. A utilização da Web como plataforma para a educação a distância (e-learning), tem sido uma das grandes alternativas para a educação em sala de aula tradicional. Embora esses sistemas sejam amplamente utilizados, existem limitações quanto à dificuldade de busca, integração e reuso dos materiais existentes. Neste contexto, neste trabalho é apresentada uma arquitetura multiagente para o desenvolvimento de sistemas Web semânticos para a gestão de conteúdos educacionais. Como parte da arquitetura foi especificado um conjunto de ontologias e agentes inteligentes, responsáveis por recuperar e integrar conteúdos educacionais, para as atividades de busca,

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Abstract. Smart Grid technologies are changing the way energy is generated, distributed and consumed. With the increasing spread of renewable power sources, new market strategies are needed to guarantee a more sustainable participation and less dependency of bulk generation. In PowerTAC (Power Trading Agent Competition), different software agents compete in a simulated energy market, impersonating broker companies to create and manage attractive tariffs for customers while aiming to profit. In this paper, we present TugaTAC Broker, a PowerTAC agent that uses a fuzzy logic mechanism to compose tariffs based on its customers portfolio. Fuzzy sets allow adaptive configurations for brokers in different scenarios. To validate and compare the performance of TugaTAC, we have run a local version of the PowerTAC competition. The experiments comprise TugaTAC competing against other simple agents and a more realistic configuration, with instances of the winners of previous editions of the competition. Preliminary results show a promising dynamic: our approach was able to manage imbalances and win the competition in the simple case, but need refinements to compete with more sophisticated market.

Industrial Cyber-Physical Systems (CPS) are promoting the development of smart machines and products, leading to the next generation of intelligent production systems. In this context, Artificial Intelligence (AI) is posed as a key enabler for the realization of CPS requirements, supporting the data analysis and the system dynamic adaptation. However, the centralized Cloud-based AI approaches are not suitable to handle many industrial scenarios, constrained by responsiveness and data sensitive. Edge Computing can address the new challenges, enabling the decentralization of data analysis along the cyber-physical components. In this context, distributed AI approaches, such those based on Multi-agent Systems (MAS), are essential to handle the distribution and interaction of the components. Based on that, this work uses a MAS approach to design cyber-physical agents that can embed different data analysis capabilities, supporting the decentralization of intelligence. These concepts were applied to an industrial automobile multi-stage production system, where different kinds of data analysis were performed in autonomous and cooperative agents disposed along Edge, Fog and Cloud computing layers.Industrial Cyber-Physical Systems (CPS) are enabling the next generation of intelligent production systems, mainly based on the concepts of smart machines and products. Driven by the needs to attend the ever-changing market trends, such digital transformation is mainly based on the use of Internet of Things (IoT), Cloud Computing and Artificial Intelligence (AI) technologies [12]. While the first enables the interconnection of equipment and consequently the digitization of the industrial environment [22], the second provides on demand high processing and storage resources [15]. On the other hand, AI provides advanced data analysis algorithms, such those based on Machine-Learning (ML), that can take advantage of the huge amounts of IoT data and the power of Cloud Computing, in order to provide actionable information and support data-driven decision-making [20,8].Although Cloud manufacturing [15] has been seen as a new paradigm in the realization of the 4th industrial revolution (4IR) [12], the traditional Cloud-based approaches, where IoT devices send all the data to be processed by centralized applications, present some drawbacks. Indeed, besides information security and privacy concerns [21], this approach is not suitable for many real-time, data-sensitive and constrained network applications [2]. In this context, Fog Computing emerged to cover the Cloud limitations, promoting the deployment of data processing capabilities closer to the data sources [4]. It defines an intermediate computing layer between Cloud applications and IoT devices that besides providing a more direct, reliable, secure and fast link between them, also promotes the decentralization of data analysis, decision-making and control, increasing local components autonomy.Besides Fog, which considers equipment at the local network, CPS also considers processing ca...

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Abstract. The industry digitization is transforming its business models, organizational structures and operations, mainly promoted by the advances and the mass utilization of smart methods, devices and products, being leveraged by initiatives like Industrie 4.0. In this context, the data is a valuable asset that can support the smart factory features through the use of Big Data and advanced analytics approaches. In order to address such requirements and related challenges, Cyber Physical Systems (CPS) promote the development of more intelligent, adaptable and responsiveness supervisory and control systems capable to overcome the inherent complexity and dynamics of industrial environments. In this context, this work presents an agent-based industrial CPS, where agents are endowed with data analysis capabilities for distributed, collaborative and adaptive process supervision and control. Additionally, to address the different industrial levels' requirements, this work combines two main data analysis scopes, at operational level, applying distributed data stream analysis for rapid response monitoring and control, and at supervisory level, applying big data analysis for decision-making, planning and optimization. Some experiments have been performed in the context of an electric micro grid where agents were able to perform distributed data analysis to predict the renewable energy production.