90 citations · 351 across the 32 of their papers we have counts for
36 papers · 1 filter
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…
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 for Fully Observable Non-Deterministic Planning Domains
Till Hofmann, Hector Geffner
General policies represent reactive strategies for solving large families of planning problems like the infinite collection of solvable instances from a given domain. Methods for l…
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…
Learning More Expressive General Policies for Classical Planning Domains
Simon Ståhlberg, Blai Bonet, Hector Geffner
GNN-based approaches for learning general policies across planning domains are limited by the expressive power of , namely; first-order logic with two variables and counting.…
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…
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…
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…
General Policies, Serializations, and Planning Width
Blai Bonet, Hector Geffner
It has been observed that in many of the benchmark planning domains, atomic goals can be reached with a simple polynomial exploration procedure, called IW, that runs in time expone…
Qualitative Numeric Planning: Reductions and Complexity
Blai Bonet, Hector Geffner
Qualitative numerical planning is classical planning extended with non-negative real variables that can be increased or decreased "qualitatively", i.e., by positive indeterminate a…
Factored Probabilistic Belief Tracking
Blai Bonet, Hector Geffner
The problem of belief tracking in the presence of stochastic actions and observations is pervasive and yet computationally intractable. In this work we show however that probabilis…
Causal Belief Decomposition for Planning with Sensing: Completeness Results and Practical Approximation
Blai Bonet, Hector Geffner
Belief tracking is a basic problem in planning with sensing. While the problem is intractable, it has been recently shown that for both deterministic and non-deterministic systems…
Generalized Planning: Non-Deterministic Abstractions and Trajectory Constraints
Blai Bonet, Giuseppe De Giacomo, Hector Geffner +1
We study the characterization and computation of general policies for families of problems that share a structure characterized by a common reduction into a single abstract problem…
Action Selection for MDPs: Anytime AO* vs. UCT
Blai Bonet, Hector Geffner
In the presence of non-admissible heuristics, A* and other best-first algorithms can be converted into anytime optimal algorithms over OR graphs, by simply continuing the search af…