Author name cluster

Guillem Francès

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

10 papers

2 author rows

AAAI Conference 2022 Conference Paper

The FF Heuristic for Lifted Classical Planning

Augusto B. Corrêa
Florian Pommerening
Malte Helmert
Guillem Francès

Heuristics for lifted planning are not yet as informed as the best heuristics for ground planning. Recent work introduced the idea of using Datalog programs to compute the additive heuristic over lifted tasks. Based on this work, we show how to compute the more informed FF heuristic in a lifted manner. We extend the Datalog program with executable annotations that can also be used to define other delete-relaxation heuristics. In our experiments, we show that a planner using the lifted FF implementation produces state-of-the-art results for lifted planners. It also reduces the gap to state-of-the-art ground planners in domains where grounding is feasible.

PDF Details

ICAPS Conference 2021 Conference Paper

Delete-Relaxation Heuristics for Lifted Classical Planning

Augusto B. Corrêa
Guillem Francès
Florian Pommerening
Malte Helmert

Recent research in classical planning has shown the importance of search techniques that operate directly on the lifted representation of the problem, particularly in domains where the ground representation is prohibitively large. In this paper, we show how to compute the additive and maximum heuristics from the lifted representation of a problem. We do this by adapting well-known reachability analysis techniques based on a Datalog formulation of the delete relaxation of the problem. Our adaptation allows us to obtain not only the desired heuristic value, but also other useful heuristic information such as helpful actions. Our empirical evaluation shows that our lifted version of the additive heuristic is competitive with its ground counterpart on most of the standard international competition benchmarks, and significantly outperforms other state-of-the-art lifted heuristic methods in the literature.

Details

AAAI Conference 2021 Conference Paper

Learning General Planning 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 policies can be computed in two steps: first, a suitable abstraction in the form of a qualitative numerical planning problem (QNP) is learned from sample plans, then the general policies are obtained from the learned QNP using a planner. In this work, we introduce an alternative approach for computing more expressive general policies which does not require sample plans or a QNP planner. The new formulation is very simple and can be cast in terms that are more standard in machine learning: a large but finite pool of features is defined from the predicates in the planning examples using a general grammar, and a small subset of features is sought for separating “good” from “bad” state transitions, and goals from non-goals. The problems of finding such a “separating surface” while labeling the transitions as “good” or “bad” are jointly addressed as a single combinatorial optimization problem expressed as a Weighted Max-SAT problem. The advantage of looking for the simplest policy in the given feature space that solves the given examples, possibly non-optimally, is that many domains have no general, compact policies that are optimal. The approach yields general policies for a number of benchmark domains.

PDF Details

IJCAI Conference 2021 Conference Paper

Learning Generalized Unsolvability Heuristics for Classical Planning

Simon Ståhlberg
Guillem Francès
Jendrik Seipp

Recent work in classical planning has introduced dedicated techniques for detecting unsolvable states, i. e. , states from which no goal state can be reached. We approach the problem from a generalized planning perspective and learn first-order-like formulas that characterize unsolvability for entire planning domains. We show how to cast the problem as a self-supervised classification task. Our training data is automatically generated and labeled by exhaustive exploration of small instances of each domain, and candidate features are automatically computed from the predicates used to define the domain. We investigate three learning algorithms with different properties and compare them to heuristics from the literature. Our empirical results show that our approach often captures important classes of unsolvable states with high classification accuracy. Additionally, the logical form of our heuristics makes them easy to interpret and reason about, and can be used to show that the characterizations learned in some domains capture exactly all unsolvable states of the domain.

PDF Details DOI

ICAPS Conference 2020 Conference Paper

Lifted Successor Generation Using Query Optimization Techniques

Augusto B. Corrêa
Florian Pommerening
Malte Helmert
Guillem Francès

The standard PDDL language for classical planning uses several first-order features, such as schematic actions. Yet, most classical planners ground this first-order representation into a propositional one as a preprocessing step. While this simplifies the design of other parts of the planner, in several benchmarks the grounding process causes an exponential blowup that puts otherwise solvable tasks out of reach of the planners. In this work, we take a step towards planning with lifted representations. We tackle the successor generation task, a key operation in forward-search planning, directly on the lifted representation using well-known techniques from database theory. We show how computing the variable substitutions that make an action schema applicable in a given state is essentially a query evaluation problem. Interestingly, a large number of the action schemas in the standard benchmarks result in acyclic conjunctive queries, for which query evaluation is tractable. Our empirical results show that our approach is competitive with the standard (grounded) successor generation techniques in a few domains and outperforms them on benchmarks where grounding is challenging or infeasible.

Details

IJCAI Conference 2019 Conference Paper

Generalized Potential Heuristics for Classical Planning

Guillem Francès
Augusto B. Corrêa
Cedric Geissmann
Florian Pommerening

Generalized planning aims at computing solutions that work for all instances of the same domain. In this paper, we show that several interesting planning domains possess compact generalized heuristics that can guide a greedy search in guaranteed polynomial time to the goal, and which work for any instance of the domain. These heuristics are weighted sums of state features that capture the number of objects satisfying a certain first-order logic property in any given state. These features have a meaningful interpretation and generalize naturally to the whole domain. Additionally, we present an approach based on mixed integer linear programming to compute such heuristics automatically from the observation of small training instances. We develop two variations of the approach that progressively refine the heuristic as new states are encountered. We illustrate the approach empirically on a number of standard domains, where we show that the generated heuristics will correctly generalize to all possible instances.

PDF Details

AAAI Conference 2019 Conference Paper

Learning Features and Abstract Actions for Computing Generalized Plans

Blai Bonet
Guillem Francès
Hector Geffner

Generalized planning is concerned with the computation of plans that solve not one but multiple instances of a planning domain. Recently, it has been shown that generalized plans can be expressed as mappings of feature values into actions, and that they can often be computed with fully observable non-deterministic (FOND) planners. The actions in such plans, however, are not the actions in the instances themselves, which are not necessarily common to other instances, but abstract actions that are defined on a set of common features. The formulation assumes that the features and the abstract actions are given. In this work, we address this limitation by showing how to learn them automatically. The resulting account of generalized planning combines learning and planning in a novel way: a learner, based on a Max SAT formulation, yields the features and abstract actions from sampled state transitions, and a FOND planner uses this information, suitably transformed, to produce the general plans. Correctness guarantees are given and experimental results on several domains are reported.

PDF Details

IJCAI Conference 2017 Conference Paper

Purely Declarative Action Descriptions are Overrated: Classical Planning with Simulators

Guillem Francès
Miquel Ramírez
Nir Lipovetzky
Hector Geffner

Classical planning is concerned with problems where a goal needs to be reached from a known initial state by doing actions with deterministic, known effects. Classical planners, however, deal only with classical problems that can be expressed in declarative planning languages such as STRIPS or PDDL. This prevents their use on problems that are not easy to model declaratively or whose dynamics are given via simulations. Simulators do not provide a declarative representation of actions, but simply return successor states. The question we address in this paper is: can a planner that has access to the structure of states and goals only, approach the performance of planners that also have access to the structure of actions expressed in PDDL? To answer this, we develop domain-independent, black box planning algorithms that completely ignore action structure, and show that they match the performance of state-of-the-art classical planners on the standard planning benchmarks. Effective black box algorithms open up new possibilities for modeling and for expressing control knowledge, which we also illustrate.

PDF Details

ICAPS Conference 2015 Conference Paper

Modeling and Computation in Planning: Better Heuristics from More Expressive Languages

Guillem Francès
Hector Geffner

Most of the key computational ideas in planning have been developed for simple planning languages where action preconditions and goals are conjunctions of propositional atoms. Preconditions and goals that do not fit into this form are normally converted into it either manually or automatically. In this work, we show that this modeling choice hides important structural information, resulting in poorer heuristics and weaker planning performance. From a theoretical point of view, we show that the direct generalization of relaxed planning graph heuristics to more expressive languages that implicitly allow conjunctions of atoms with more than one state variable leaves open a crisp gap, as it fails to properly account for the constraints over these variables. The simple propositional languages that are standard in planning do not remove this gap but “hide it under the rug” by forcing atoms to be of the form X=c, where c is a constant and X is a (usually boolean) state variable. Closing this gap in the computation of the relaxed planning graph for more expressive languages leads to a more accurate but intractable heuristic, yet a cost-effective tradeoff can be achieved using local forms of constraint propagation that result in better heuristics, better plans, and a more effective search. We show this empirically over a diverse set of illustrative examples using a fragment of the Functional STRIPS planning language.

Details

EUMAS Conference 2014 Conference Paper

Decision Making in Agent-Based Models

Guillem Francès
Xavier Rubio-Campillo
Carla Lancelotti
Marco Madella

Abstract Agent-Based Models (ABM) are being increasingly applied to the study of a wide range of social phenomena, often putting the focus on the macroscopic patterns that emerge from the interaction of a number of agents programmed to behave in a plausible manner. This agent behavior, however, is all too often encoded as a small set of rules that produces a somewhat simplistic behavior. In this short paper, we propose to explore the impact of decision-making processes on the outcome of simulations, and introduce a type of agent that uses a more systematic and principled decision-making approach, based on casting the simulation environment as a Markov Decision Process. We compare the performance of this type of agent to that of more simplistic agents on a simple ABM simulation, and examine the interplay between the decision-making mechanism and other relevant simulation parameters such as the distribution and scarcity of resources. Our preliminary findings show that our novel agent outperforms the rest of agents, and, more generally, that the process of decision-making needs to be acknowledged as a first-class parameter of ABM simulations with a significant impact on the simulation outcome.

Details