Modern machine learning algorithms crucially rely on several design decisions to achieve
strong performance, making the problem of Hyperparameter Optimization (HPO) more …

Exploratory landscape analysis (ELA) supports supervised learning approaches for
automated algorithm selection and configuration by providing sets of features that quantify …

Existing studies in black-box optimization suffer from low generalizability, caused by a
typically selective choice of problem instances used for training and testing of different …

Automated planning is a prominent Artificial Intelligence challenge, as well as being a
common capability requirement for intelligent autonomous agents. A critical aspect of what is …

The International Planning Competition (IPC) is a prominent event of the artificial
intelligence planning community that has been organized since 1998; it aims at fostering the …

Best-first search algorithms such as A* need to apply tie-breaking strategies in order to
decide which node to expand when multiple search nodes have the same evaluation score …

Despite recent improvements in search techniques for cost-optimal classical planning, the
exponential growth of the size of the search frontier in A* is unavoidable. We investigate …

Recent advances in automated planning are leading towards the use of planning engines in
a wide range of real-world applications. As the exploitation of planning techniques in …

Solver competitions play a prominent role in assessing and advancing the state of the art for
solving many problems in AI and beyond. Notably, in many areas of AI, competitions have …

The theory of bit-vectors in SMT solvers is very important for many applications due to its
ability to faithfully model the behavior of machine instructions. A crucial step in solving bit …