Collection

Engineering: Artificial Intelligence in Smart Computing, Industry and Societies

Submission status
Open
Open for submission from
16 April 2023
Submission deadline
01 August 2024
The Topical Collection looks for submissions covering the following aspects: (1) Artificial Intelligence in Smart Computing; (2) Artificial Intelligence for Industry and Societies; (3) Multi-Agent Systems (Theory and Applications); (4) Social Simulation and Modelling. Further detail of the different subject areas is given below. (1) Artificial Intelligence in Smart Computing: With the advance of computer networks and hardware technology, it is now possible not only to integrate miniaturized computers into the things in our surroundings but also possible to connect those things over computer networks and the Internet. (2) Artificial Intelligence for Industry and Societies: Societies and industries face many challenges to be more intelligent and sustainable. In this context, many good practices and approaches are being explored, and numerous new contributions are being generated daily. Many good practices and procedures are being investigated and several unique contributions emerge without benefitting from sufficient visibility. In these, A.I. will have an even stronger role, as complex multi-disciplinary projects need its support. In this evolution, Industry and Societies must be holding hands, and this is a truly multi-disciplinary endeavour, whereby A.I. is a key hinge and facilitator between technologies, goods and services. (3) Multi-Agent Systems: Theory and Applications: The purpose of this thematic track is to provide a high-profile, internationally respected discussion forum on the most recent and innovative scientific research in the area of agents and multiagent systems (MAS). The MASTA thematic track will cover not only traditional topics related to agent theory and multi-agent engineering but also issues associated with using evolving autonomous systems in real-world scenarios where humans are involved, including conversational systems. (4) Social Simulation and Modelling: Social Simulation is a multi-disciplinary effort that has increasingly established new challenges for the Artificial Intelligence and Multiagent Systems (MAS) community, by bringing the agent technology to face complex phenomena such as the ones found in social sciences. In addition, since social life could not be conceived without social interactions, other areas such as social network analysis have contributed to characterising and modelling the structures of networks, so as to understand the flow of relevant factors between network nodes (i.e., the agents).

Editors

  • Sherin Moussa Sherin Moussa , 

    Sherin Moussa

    Sherin Moussa, Professor, Université Française d’Égypte, Egypt. Her research involves IoT, big data analytics, software engineering, and privacy preservation. She is a consultant to many organizations, with industrial experience through national/regional projects. She is the Founder of Next Scholar Generation research group, co-organizing workshops & conferences.

  • Carlos Filipe Portela Carlos Filipe Portela , 

    Carlos Filipe Portela

    Carlos Filipe Portela, Researcher, University of Minho, Portugal. Filipe Portela has published numerous academic papers and articles on data science, gamification, pervasive systems and smart cities. He holds a patent in Data Science and is the Principal Investigator and manager of several R&D&I projects. He is also an editor and author of technical books and articles, on IOTech - Innovation on Technology.

  • João Balsa João Balsa  &

    João Balsa

    João Balsa, Assistant Professor, University of Lisbon, Portugal. His research has been in the area of artificial intelligence in two main strands: in the area of multi-agent systems, namely of agent-based simulation models, with applications to diverse domains, like the definition of public policies aiming an improvement of social welfare; also, in the definition of models underlying the development of intelligent conversational agents for healthcare.

  • Pedro Campos Pedro Campos

    Pedro Campos

    Pedro Campos, Assistant Professor, University of Porto, Portugal. His areas of interest are Data Science, Machine Learning, Market Research, Social Network Analysis and Computational Economics with applications to Demography, Enterprise Networks, Marketing and Population Dynamics.

Articles (6 in this collection)