4 accepted papers
GNN-based approaches for learning general policies across planning domains are limited by the expressive power of C2, namely; first-order logic with two variables and counting. This limitation can be overcomed by transitioning to k-GNNs, for k=3, wherein object embeddings are substituted with triple…
The expressive power of graph learning architectures based on the $k$-dimensional Weisfeiler-Leman ($k$-WL) hierarchy is well understood. However, such architectures often fail to deliver solid predictive performance on real-world tasks, limiting their practical impact. In contrast, global attention…
It has been observed that in many of the benchmark planning domains, atomic goals can be reached with a simple polynomial exploration procedure, called IW, that runs in time exponential in the problem width. Such problems have indeed a bounded width: a width that does not grow with the number of pro…
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 policies can be computed in two steps: first, a suitable abstraction in the form of a qualitative numerical planning proble…