90 citations · 343 across the 29 of their papers we have counts for
35 papers
Learning How to Search for Plans with Exponentially Less Space
Dominik Drexler, Simon Ståhlberg, Markus Fritzsche +1
Heuristic search for a plan can store exponentially many states, even when its heuristic is almost perfect. We instead learn search control, one specification per domain, written a…
Recurrent Graph Neural Networks with Set-Based Aggregation
Blai Bonet
Recurrent GNNs iterate message passing to convergence, and their logical characterizations to date rely on multi-set aggregation, graded (counting) logics, and halting or acceptanc…
Learning General Policies with Policy Gradient Methods
Simon Ståhlberg, Blai Bonet, Hector Geffner
While reinforcement learning methods have delivered remarkable results in a number of settings, generalization, i.e., the ability to produce policies that generalize in a reliable…
Learning General Policies From Examples
Blai Bonet, Hector Geffner
Combinatorial methods for learning general policies that solve large collections of planning problems have been recently developed. One of their strengths, in relation to deep lear…
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…
On Policy Reuse: An Expressive Language for Representing and Executing General Policies that Call Other Policies
Blai Bonet, Dominik Drexler, Hector Geffner
Recently, a simple but powerful language for expressing and learning general policies and problem decompositions (sketches) has been introduced in terms of rules defined over a set…