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 …

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 …

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 …

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 …

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

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

Current approaches for learning for planning have yet to achieve competitive performance
against classical planners in several domains, and have poor overall performance. In this …

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 …

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 …

Planning domains are an important notion, eg when it comes to restricting the input for
generalized planning or learning approaches. However, domains as specified in PDDL …