Learning Dynamic Algorithms for Automated Planning (LDAAP)

Connecting the fields of model-based reasoning and data-driven learning has recently been identified as one of the key research goals in artificial intelligence. Our project will contribute to this endeavor, focusing on the area of automated planning. We will learn heuristic functions that guarantee optimal solutions and planning algorithms that dynamically adapt to the given task.

PI: Jendrik Seipp

Core team: Paul Höft, David Speck

Funding: This project is partially supported by the Wallenberg AI, Autonomous Systems and Software Program (WASP) funded by the Knut and Alice Wallenberg Foundation.