Integrating hardware agents into an enhanced multi-agent architecture for Ambient Intelligence systems

Ambient Intelligence (AmI) systems require the integration of complex and innovative solutions. In this sense, agents and multi-agent systems have characteristics such as autonomy, reasoning, reactivity, social abilities and pro-activity which make them appropriate for developing distributed systems based on Ambient Intelligence. In addition, the use of context-aware technologies is an essential aspect in these developments in order to perceive stimuli from the context and react to it autonomously. This paper presents the integration of the Hardware-Embedded Reactive Agents (HERA) Platform into the Flexible and User Services Oriented Multi-agent Architecture (FUSION@), a multi-agent architecture for developing AmI systems that integrates intelligent agents with a service-oriented architecture approach. Because of this integration, FUSION@ has the ability to manage both software and hardware agents by using self-adaptable heterogeneous wireless sensor networks. Preliminary results presented in this paper demonstrate the feasibility of FUSION@ as a future alternative for developing Ambient Intelligence systems where users and systems can use both software and hardware agents in a transparent way, achieving a higher level of ubiquitous computing and communication

Implementation of context-aware workflows with Multi-agent Systems

Systems in Ambient Intelligence (AmI) need to manage workflows that represent users’ activities. These workflows can be quite complex, as they may involve multiple participants, both physical and computational, playing different roles. Their execution implies monitoring the development of the activities in the environment, and taking the necessary actions for them and the workflow to reach a certain end. The context-aware approach supports the development of these applications to cope with event processing and regarding information issues. Modeling the actors in these context-aware workflows, where complex decisions and interactions must be considered, can be achieved with multi-agent systems. Agents are autonomous entities with sophisticated and flexible behaviors, which are able to adapt to complex and evolving environments, and to collaborate to reach common goals. This work presents architectural patterns to integrate agents on top of an existing context-aware architecture. This allows an additional abstraction layer on top of context-aware systems, where knowledge management is performed by agents.This approach improves the flexibility of AmI systems and facilitates their design. A case study on guiding users in buildings to their meetings illustrates this approach

Smart feeding in farming through IoT in silos

Smart farming practices are of utmost importance for any economy to foster its growth and development and tackle problems like hunger and food insecurity and ensure the well-being of its citizens. However, such practices usually require large investments that are not affordable for SMEs. Such is the case of expensive weighing machines for silos, while the range of possibilities of the Internet of Things (IoT) could intensively reduce these costs while connecting the data to intelligent Cloud services, such as smart feeding systems. The paper presents a novel IoT device and methodology to monitor quantity and quality of grains in silo by estimating the volume of grains at different time instants along with temperature and humidity in the silo. A smart feeding system, implemented via a virtual organization of agents, processes the data and regulates the grain provided to the animals. Experimental on-field measurements at a rabbit farm show the suitability of the proposed system to reduce waste as well as animal diseases and mortality

Efficiency and Reliability in Bringing AI into Transport and Smart Cities Solutions

capacity and the low cost of the Cloud have facilitated the development of new, powerful algorithms. The efficiency of these algorithms in Big Data processing, Deep Learning and Convolutional Networks is transforming the way we work and is opening new horizons. Thanks to them, we can now analyse data and obtain unimaginable solutions to today’s problems. Nevertheless, our success is not entirely based on algorithms, it also comes from our ability to follow our “gut” when choosing the best combination of algorithms for an intelligent artefact. Their development involves the use of both connectionist and symbolic systems, that is to say data and knowledge. Moreover, it is necessary to work with both historical and real-time data. It is also important to consider development time, costs and the ability to create systems that will interact with their environment, will connect with the objects that surround them and will manage the data they obtain in a reliable manner. In this keynote, the evolution of intelligent computer systems will be examined, especially that of convolutional networks. The need for human capital will be discussed, as well as the need to follow one’s “gut instinct” in problem-solving. Furthermore, the importance of IoT and Blockchain in the development of intelligent systems will be analysed and it will be shown how tools like "Deep Intelligence" make it possible to create computer systems efficiently and effectively. "Smart" infrastructures need to incorporate all added-value resources so they can offer useful services to the society, while reducing costs, ensuring reliability and improving the quality of life of the citizens. The combination of AI with IoT and with blockchain offers a world of possibilities and opportunities. The development of transport, smart cities, urbanizations and leisure areas can be improved through the use of distributed intelligent computer systems. In this regard, edge platforms or fog computing help increase efficiency, reduce network latency, improve security and bring intelligence to the edge of the network, the sensors, users and the environment. Several use cases of intelligent systems will be presented, and it will be analysed how the processes of implementation and use have been optimized by means of different tools