9 citations · 10 across the 7 of their papers we have counts for
3 papers · 1 filter
Learning to Ground Existentially Quantified Goals
Martin Funkquist, Simon Ståhlberg, Hector Geffner
Goal instructions for autonomous AI agents cannot assume that objects have unique names. Instead, objects in goals must be referred to by providing suitable descriptions. However,…
Symmetries and Expressive Requirements for Learning General Policies
Dominik Drexler, Simon Ståhlberg, Blai Bonet +1
State symmetries play an important role in planning and generalized planning. In the first case, state symmetries can be used to reduce the size of the search; in the second, to re…
Learning More Expressive General Policies for Classical Planning Domains
Simon Ståhlberg, Blai Bonet, Hector Geffner
GNN-based approaches for learning general policies across planning domains are limited by the expressive power of , namely; first-order logic with two variables and counting.…