9 citations · 9 across the 3 of their papers we have counts for
3 papers
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 Generalized Policies Without Supervision Using GNNs
Simon Ståhlberg, Blai Bonet, Hector Geffner
We consider the problem of learning generalized policies for classical planning domains using graph neural networks from small instances represented in lifted STRIPS. The problem h…