Aachen Symposium

Assistant professor at USC. He focuses on AI for planning problems, solution explanation, and knowledge discovery using deep learning, reinforcement learning, and heuristic search.

Retired professor from Universidad Simón Bolívar and researcher at UPF. He researches decision-theoretic planning, heuristic search, reinforcement learning, and graphical models.

PhD student at LAAS-CNRS, supervised by Sylvie Thiébaux. His research spans machine learning and planning, heuristics, graph neural networks, and nondeterministic planning.

Professor at the University of Oxford. His research spans knowledge representation, reasoning about actions, planning, autonomous agents, and data management.

PhD student at LiU. His research focuses on integrating learning with automated planning, particularly learning reusable control knowledge for goal-oriented action sequences.

Assistant professor at TU Delft. He researches program synthesis and probabilistic programming for minimal data learning, applied to scientific discovery, robotics, and planning.

Assistant professor at VU Amsterdam. He researches deep and reinforcement learning, focusing on representation learning for agents that generalize from limited data.

Professor at UniBS. He researches issues of automated planning, knowledge representation and reasoning, machine learning and data mining.

Associate professor at UPF. His interests include machine learning, approximate inference, and optimal control, applied to social networks, robotics, and brain-computer interfaces.

Associate professor at ANU. His research covers automated planning, reasoning, and machine learning, with applications in security, retail optimization, and robotics.

Full professor at UPF. His research focuses on automated planning, reinforcement learning, multi-agent systems, and the computational complexity of these problems.

Associate professor at Technion. He specializes in artificial intelligence and robotics, including autonomous decision support systems and planning.

Assistant professor at KAIST. His research focuses on developing general-purpose mobile manipulation robots that can efficiently make decisions in complex environments.

Associate professor at Brown and director of the intelligent robot lab. He researches general-purpose robots and learning abstraction hierarchies for efficient planning.

Researcher at Google DeepMind. He is interested in learning representation, schemas, and cognitive maps, planning, and variational inference in ML and cognitive neuroscience.

Assistant professor at the U of A. He develops algorithms for combinatorial search, focusing on programmatic representations for sequential decision-making.

Professor at the U of T. Her research encompasses sequential decision-making, human-compatible AI, commonsense reasoning, and safety constraints in AI.

Professor at Leiden University. He leads the sustainable energy learning lab and the reinforcement learning group, focusing on artificial intelligence.

Professor at Wits University. He researches reinforcement learning and decision-making in autonomous systems, focusing on knowledge abstraction and generalization.

Professor at the U of T. His research spans machine learning, AI, and operations research, with applications in conversational systems, Smart Cities, and predictive analytics.

Head of the data and knowledge management research unit at FBK. He researches multi-context systems, semantic matching, and integrating machine learning and logical reasoning.

Assistant professor at CTU, specializing in relational machine learning. His research involves integrating relational logic with deep learning on data with complex dependencies.

Assistant professor at UM. His research focuses on reinforcement learning, search algorithms, and AI in games, with additional interests in game description languages.

Associate professor at ASU. He researches generalizable planning and learning in AI and robotics, with an emphasis on AI system assessment, safety, and transfer learning.

Postdoc at RWTH, previously principal research engineer at Linköping University. His research focuses on using neural networks to learn generalized policies for classical planning.

Associate professor at Technion and head of the robot learning lab. He studies AI and machine learning for robotics, focusing on reinforcement learning and environment modeling.

Associate professor at the U of A. He focuses on fundamental and applied problems in reinforcement learning, human-in-the-loop AI, multi-agent systems, and robotics.

Professor at ANU and a researcher at the University of Toulouse. She specializes in automated planning, scheduling, and optimization, with applications in energy and transport.

Professor at TU Berlin. His research integrates machine learning and optimization in robotics, addressing physical reasoning, task-and-motion planning, and adaptive behavior.

Director of strategic planning at FBK. His interests span AI and automated planning, software services, and automated verification and synthesis of software systems.

Assistant professor at UvA. He focuses on reinforcement learning with structured data and prior knowledge, aiming to improve data efficiency for combinatorial optimization.