It has been recently shown that general policies for many classical planning domains can be
expressed and learned in terms of a pool of features defined from the domain predicates …

While reinforcement learning methods have delivered remarkable results in a number of
settings, generalization, ie, the ability to produce policies that generalize in a reliable and …

Manufacturing companies face challenges when it comes to quickly adapting their
production control to fluctuating demands or changing requirements. Control approaches …

We consider the problem of learning generalized policies for classical planning domains
using graph neural networks from small instances represented in lifted STRIPS. The …

In this paper, we discuss the learning of generalised policies for probabilistic and classical
planning problems using Action Schema Networks (ASNets). The ASNet is a neural network …

Recently, sketches have been introduced as a general language for representing the
subgoal structure of instances drawn from the same domain. Sketches are collections of …

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 …

Graph learning is naturally well suited for use in symbolic, object-centric planning due to its
ability to exploit relational structures exhibited in planning domains and to take as input …

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 …

Although heuristic search is one of the most successful approaches to classical planning,
this planning paradigm does not apply straightforwardly to Generalized Planning (GP) …