From 2 November 2020, the autumn semester 2020 will take place online. Exceptions: Courses that can only be carried out with on-site presence. Please note the information provided by the lecturers via e-mail.
This course introduces modeling and simulation techniques for multi-agent systems in the era of Internet of Things. Topics such as collective intelligence and decentralized combinatorial optimization are covered. Students will prototype autonomous self-organizing agents to tackle techno-socio-economic challenges in application domains of smart cities and beyond.
The learning objectives of this course is to teach how to model, design and build self-organizing (multi-)agent systems in distributed techno-socio-economic systems such as smart grids, smart cities, pedestrian flows, traffic systems, and others. Students will be especially prepared to apply such systems in the era of Internet of Things, Big Data and distributed sharing economies. For this reason, students will experiment will real-world data as well as simulation and prototyping software with which they will examine and measure emergent phenomena such as traffic jams or power cascading failures. Τhe course stretches from simple, reactive agents to more sophisticated, decision-making or cognitive agents. The ultimate goal is to construct mechanims based on state of the art distributed optimization and machine learning techniques to improve collective and system-wide objectives related to reliability, resilience, sustainability, fairness and others.