Current research projects

• Representation Learning for Acting and Planning (RLeap)
• Learning Dynamic Algorithms for Automated Planning (LDAAP)
• Learning Trustworthy Planning Algorithms (LTPA)
• Collaborative Constraint-Based Planning (CCBP)
• Symbolic Search for Diverse Plans and Maximum Utility (SSDPMU)
• Neuro-Symbolic AI for Improving Energy Efficiency in 6G (AI6G)
• Robust Planning with Large Language Models (LLMPlan)
• AI for Attack Identification, Response and Recovery (AIR²)

General research topics

Within our lab, we develop machines that can reason and act in complex environments. Our primary research area is Automated Planning, which we complement with techniques from Machine Learning, Combinatorial Optimization and Operations Research. Our main topics of interest include:

• Theory of Planning: We contribute to the theoretical foundations of Automated Planning, studying the complexity of planning problems and algorithms.
• Learning Planning Models: We develop algorithms that extract the dynamics of an observed environment to learn compact descriptions of planning tasks.
• Efficient Planning Algorithms: We design and implement scalable planning algorithms, mainly based on heuristic state-space search.
• Generalized Planning: We create methods for learning how to solve a whole class of tasks efficiently.
• Planning and Reinforcement Learning: We combine the interpretability of planning with the flexibility of reinforcement learning.

In summary, we strive to create AI systems that efficiently solve intricate sequential decision-making problems, based on solid theoretical foundations and practical algorithms.