The performance of many algorithms in the fields of hard combinatorial problem solving,
machine learning or AI in general depends on parameter tuning. Automated methods have …

Recent advances in reinforcement learning (RL) have led to a growing interest in applying
RL to classical planning domains or applying classical planning methods to some complex …

Computing goal-directed behavior is essential to designing efficient AI systems. Due to the
computational complexity of planning, current approaches rely primarily upon hand-coded …

State-of-the-art algorithms for solving hard computational problems often expose many
parameters whose settings critically affect empirical performance. Manually exploring the …

Many fields of computational science advance through improvements in the algorithms used
for solving key problems. These advancements are often facilitated by benchmarks and …

The performance of many hard combinatorial problem solvers depends strongly on their
parameter settings, and since manual parameter tuning is both tedious and suboptimal the …

Modern solvers for hard computational problems often expose parameters that permit
customization for high performance on specific instance types. Since it is tedious and time …

To achieve peak performance, it is often necessary to adjust the parameters of a given
algorithm to the class of problem instances to be solved; this is known to be the case for …

Algorithmic scheme 1: generate and evaluate initial set of configurations 卷 0 2: choose best-
so-far configuration B⇤ 2 卷 0 3: while tuning budget available do 4: learn surrogate model …

To achieve peak performance of an algorithm (in particular for problems in AI), algorithm
configuration is often necessary to determine a well-performing parameter configuration. So …