DateTuesday April 12, 2022 from 12:00 PM to 1:00 PM
LocationOnline - MS Teams
Many TU/e researchers are advancing or using Artificial Intelligence in their research projects. To support cross disciplinary learning and to strengthen the TU/e AI network, EAISI organizes a series of (internal) meetings during lunch time, where various researchers talk about their projects, followed by Q&A.
Shane O'Seasnáin | Director Program Board, EAISI
Margriet van Schijndel | Program Director Smart Mobility, EAISI
Welcome & introduction
Niek Boksebeld, 'Business & Autonomous Driving' at Team Polar, student at Department EE
Artificial intelligence driving independently on Antarctica
Mauro Salazar | Assistant Professor at Control Systems Technology, Department of ME
Urgency-aware Optimal Routing in Repeated Games through Artificial Currencies
Tom van Woensel | Full Professor at Freight Transport & Logistics, Department IE&IS
AI Planner of the Future
A key aspect of Team Polar’s rover is that it should make research on Antarctica fully autonomous, making research a lot safer and affordable. For this the rover will use radar and potentially camera's and lidar to detect obstacles and plan a route around them. This way it can go from A to B safely without any hassle for the researchers.
Tom van Woensel
The AI (Artificial Intelligence) PLANNER OF THE FUTURE research program focuses on the increasing intertwining of technology and human aspects in the context of AI planning for supply chains and logistics. We combine the extensive knowledge of researchers from all multi-disciplinary IE&IS domains and the real-life living labs of European Supply Chain Forum companies from diverse industries. All industries are involved: fast-moving consumer goods, omnichannel retailing, last-mile logistics, services, health, transport and mobility, high-tech industries...
When people choose routes minimizing their individual delay, the aggregate congestion can be much higher compared to that experienced by a centrally-imposed routing. Yet centralized routing is incompatible with the presence of self-interested users. How can we reconcile the two? In this talk we address this question within a repeated game framework and propose a fair incentive mechanism based on artificial currencies that routes selfish users in a system-optimal fashion, while accounting for their temporal preferences. We instantiate the framework in a parallel-network whereby users commute repeatedly (e.g., daily) from a common start node to the end node. Thereafter, we focus on the specific two-arcs case whereby, based on an artificial currency, the users are charged when traveling on the first, fast arc, whilst they are rewarded when traveling on the second, slower arc. We assume the users to be rational and model their choices through a game where each user aims at minimizing a combination of today's discomfort, weighted by their urgency, and the average discomfort encountered for the rest of the period (e.g., a week). We show that, if prices of artificial currencies are judiciously chosen, the routing pattern converges to a system-optimal solution, while accommodating the users' urgency. We complement our study through numerical simulations. Our results show that it is possible to achieve a system-optimal solution whilst significantly reducing the users' perceived discomfort when compared to a centralized optimal but urgency-unaware policy.