Publications (44)
General Policies, Subgoal Structure, and Planning Width
Blai Bonet, Hector Geffner
It has been observed that many classical planning domains with atomic goals can be solved by means of a simple polynomial exploration procedure, called IW, that runs in time expone…
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…
From Next Token Prediction to (STRIPS) World Models
Carlos Núñez-Molina, Vicenç Gómez, Hector Geffner
We study whether next-token prediction can yield world models that truly support planning, in a controlled symbolic setting where propositional STRIPS action models are learned fro…
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…
Target Languages (vs. Inductive Biases) for Learning to Act and Plan
Hector Geffner
Recent breakthroughs in AI have shown the remarkable power of deep learning and deep reinforcement learning. These developments, however, have been tied to specific tasks, and prog…
Compiling Uncertainty Away in Conformant Planning Problems with Bounded Width
Hector Palacios, Hector Geffner
Conformant planning is the problem of finding a sequence of actions for achieving a goal in the presence of uncertainty in the initial state or action effects. The problem has been…