Automated decision-making is a key capability of any intelligent system. Intelligent systems (e.g. for traffic planning, energy systems, robots, automated negotiation) must plan ahead, which is particularly challenging when either the future is uncertain, or they interact with many other agents.

To tackle such problems, the Algorithmics group focuses on two lines of research.

• We develop new algorithms and solvers for automated planning and scheduling, improve upon existing techniques such as reinforcement learning, Markov decision processes, and mathematical programming, and integrate our results in working prototypes and toolboxes to be used by other researchers and industry.
• Furthermore, we develop game-theoretical mechanisms which govern the interaction between multiple self-interested systems or users, and provide computationally efficient algorithms to compute equilibria in these interactions.

By tackling these fundamental challenges at the intersection of sequential decision-making, machine learning, and algorithmic mechanism design, we contribute to more efficient energy use, reducing highway congestion, and reducing the cost of asset maintenance in various industries. Our results are published in the internationally leading conferences and journals in planning and scheduling, multi-agent systems, and artificial intelligence.