Planning for real-world applications requires algorithms and tools with the ability to handle
the complexity such scenarios entail. However, meeting the needs of such applications …

Extending the classical planning formalism with state-dependent action costs (SDAC) allows
an up to exponentially more compact task encoding. Recent work proposed to use edge …

Symbolic representations have attracted significant attention in optimal planning. Binary
Decision Diagrams (BDDs) form the basis for symbolic search algorithms. Closely related …

We present a principled way of extending a classical AI planning formalism with systems of
state constraints, which relate-sometimes determine-the values of variables in each state …

Soft goals in planning are optional objectives that should be achieved in the terminal state.
However, failing to achieve them does not result in the plan becoming invalid. State …

Abstraction heuristics are a popular method to guide optimal search algorithms in classical
planning. Cost partitionings allow to sum heuristic estimates admissibly by partitioning …

Observation decoding aims at discovering the underlying state trajectory of an acting agent
from a sequence of observations. This task is at the core of various recognition activities that …

Axioms are an extension for classical planning models that allow for modeling complex
preconditions and goals exponentially more compactly. Although axioms were introduced in …

Modern industrial robots are increasingly deployed in dynamic environments, where
unpredictable events are expected to impact the robot's operation. Under these conditions …

While state-dependent action costs are practically relevant and have been studied before, it
is still unclear if they are an essential feature of planning tasks. In this paper, we study to …