Current heuristic estimators for classical domain-independent planning are usually based
on one of four ideas: delete relaxations, critical paths, abstractions, and, most recently …

Liste der Autoren Page 1 Liste der Autoren Clemens Beckstein Gerhard Brewka Christian
Borgelt Wolfram Burgard Hans-Dieter Burkhard Stephan Busemann Thomas Christaller Leonie …

We present a hierarchical planning system and its application to robotic manipulation. The
novel features of the system are: 1) it finds high-quality kinematic solutions to task-level …

In imitation learning for planning, parameters of heuristic functions are optimized against a
set of solved problem instances. This work revisits the necessary and sufficient conditions of …

This paper deals with the problem of classical planning for multiple cooperative agents who
have private information about their local state and capabilities they do not want to reveal …

Abstract Previous research in Artificial Intelligence has identified the possibility of simplifying
planning problems via the identification and exploitation of symmetries. We advance the …

Anytime search algorithms solve optimisation problems by quickly finding a (usually
suboptimal) first solution and then finding improved solutions when given additional time. To …

We present MM, the first bidirectional heuristic search algorithm whose forward and
backward searches are guaranteed to''meet in the middle'', ie never expand a node beyond …

Focal search (FS) is a bounded-suboptimal search (BSS) variant of A*. Like A*, it uses an
open list whose states are sorted in increasing order of their f-values. Unlike A*, it also uses …

Value iteration is a powerful yet inefficient algorithm for Markov decision processes (MDPs)
because it puts the majority of its effort into backing up the entire state space, which turns out …