90 citations · 351 across the 32 of their papers we have counts for
4 papers · 1 filter
Language-Based Causal Representation Learning
Blai Bonet, Hector Geffner
Consider the finite state graph that results from a simple, discrete, dynamical system in which an agent moves in a rectangular grid picking up and dropping packages. Can the state…
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…
Learning First-Order Symbolic Planning Representations That Are Grounded
Andrés Occhipinti Liberman, Blai Bonet, Hector Geffner
Two main approaches have been developed for learning first-order planning (action) models from unstructured data: combinatorial approaches that yield crisp action schemas from the…
Learning Sketches for Decomposing Planning Problems into Subproblems of Bounded Width: Extended Version
Dominik Drexler, Jendrik Seipp, Hector Geffner
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 rules of t…