One of the main obstacles for developing flexible AI systems is the split between data-based
learners and model-based solvers. Solvers such as classical planners are very flexible and …

Generalized planning is concerned with the computation of general policies that solve
multiple instances of a planning domain all at once. It has been recently shown that these …

Landmarks are one of the most effective search heuristics for classical planning, but largely
ignored in generalized planning. Generalized planning (GP) is usually addressed as a …

Recently Bonet and Geffner have shown that first-order representations for planning
domains can be learned from the structure of the state space without any prior knowledge …

We present pyRDDLGym, a Python framework for auto-generation of OpenAI Gym
environments from RDDL declerative description. The discrete time step evolution of …

It has been observed that many classical planning domains with atomic goals can be solved
by means of a simple polynomial exploration procedure, called IW, that runs in time …

Qualitative numerical planning is classical planning extended with non-negative real
variables that can be increased or decreased" qualitatively", ie, by positive indeterminate …

In this article, we present an overview of the 2023 International Planning Competition. It
featured five distinct tracks designed to assess cutting‐edge methods and explore the …

This paper revisits probabilistic, model-based goal recognition to study the implications of
the use of nominal models to estimate the posterior probability distribution over a finite set of …

Planning for multi-robot coordination during long horizon missions in complex environments
need to consider resources, temporal constraints, and uncertainty. This could be …