90 citations · 351 across the 32 of their papers we have counts for
6 papers · 1 filter
Learning General Optimal Policies with Graph Neural Networks: Expressive Power, Transparency, and Limits
Simon Ståhlberg, Blai Bonet, Hector Geffner
It has been recently shown that general policies for many classical planning domains can be expressed and learned in terms of a pool of features defined from the domain predicates…
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…
Learning First-Order Representations for Planning from Black-Box States: New Results
Ivan D. Rodriguez, Blai Bonet, Javier Romero +1
Recently Bonet and Geffner have shown that first-order representations for planning domains can be learned from the structure of the state space without any prior knowledge about t…
Expressing and Exploiting the Common Subgoal Structure of Classical Planning Domains Using Sketches: Extended Version
Dominik Drexler, Jendrik Seipp, Hector Geffner
Width-based planning methods deal with conjunctive goals by decomposing problems into subproblems of low width. Algorithms like SIW thus fail when the goal is not easily serializab…
Flexible FOND Planning with Explicit Fairness Assumptions
Ivan D. Rodriguez, Blai Bonet, Sebastian Sardina +1
We consider the problem of reaching a propositional goal condition in fully-observable non-deterministic (FOND) planning under a general class of fairness assumptions that are give…
Learning General Policies from Small Examples Without Supervision
Guillem Francès, Blai Bonet, Hector Geffner
Generalized planning is concerned with the computation of general policies that solve multiple instances of a planning domain all at once. It has been recently shown that these pol…