Generalized Planning as Heuristic Search: A new planning search-space that leverages pointers over objects

Planning as heuristic search is one of the most successful approaches to classical planning but unfortunately, it does not extend trivially to Generalized Planning (GP). GP aims to compute algorithmic solutions that are valid for a set of classical planning instances from a given domain, even if these instances differ in the number of objects, the number of state variables, their domain size, or their initial and goal configuration. The generalization requirements of GP make it impractical to perform the state-space search that is usually implemented by heuristic planners. This paper adapts the planning as heuristic search paradigm to the generalization requirements of GP, and presents the first native heuristic search approach to GP. First, the paper introduces a new pointer-based solution space for GP that is independent of the number of classical planning instances in a GP problem and the size of those instances (i.e. the number of objects, state variables and their domain sizes). Second, the paper defines a set of evaluation and heuristic functions for guiding a combinatorial search in our new GP solution space. The computation of these evaluation and heuristic functions does not require grounding states or actions in advance. Therefore our GP as heuristic search approach can handle large sets of state variables with large numerical domains, e.g.~integers. Lastly, the paper defines an upgraded version of our novel algorithm for GP called Best-First Generalized Planning (BFGP), that implements a best-first search in our pointer-based solution space, and that is guided by our evaluation/heuristic functions for GP.Comment: Under review in the Artificial Intelligence Journal (AIJ

Generalized Planning with Positive and Negative Examples

Generalized planning aims at computing an algorithm-like structure (generalized plan) that solves a set of multiple planning instances. In this paper we define negative examples for generalized planning as planning instances that must not be solved by a generalized plan. With this regard the paper extends the notion of validation of a generalized plan as the problem of verifying that a given generalized plan solves the set of input positives instances while it fails to solve a given input set of negative examples. This notion of plan validation allows us to define quantitative metrics to asses the generalization capacity of generalized plans. The paper also shows how to incorporate this new notion of plan validation into a compilation for plan synthesis that takes both positive and negative instances as input. Experiments show that incorporating negative examples can accelerate plan synthesis in several domains and leverage quantitative metrics to evaluate the generalization capacity of the synthesized plans.Comment: Accepted at AAAI-20 (oral presentation

STRIPS Action Discovery

The problem of specifying high-level knowledge bases for planning becomes a hard task in realistic environments. This knowledge is usually handcrafted and is hard to keep updated, even for system experts. Recent approaches have shown the success of classical planning at synthesizing action models even when all intermediate states are missing. These approaches can synthesize action schemas in Planning Domain Definition Language (PDDL) from a set of execution traces each consisting, at least, of an initial and final state. In this paper, we propose a new algorithm to unsupervisedly synthesize STRIPS action models with a classical planner when action signatures are unknown. In addition, we contribute with a compilation to classical planning that mitigates the problem of learning static predicates in the action model preconditions, exploits the capabilities of SAT planners with parallel encodings to compute action schemas and validate all instances. Our system is flexible in that it supports the inclusion of partial input information that may speed up the search. We show through several experiments how learned action models generalize over unseen planning instances.Comment: Presented to Genplan 2020 workshop, held in the AAAI 2020 conference (https://sites.google.com/view/genplan20) (2021/03/05: included missing acknowledgments

Online action recognition

Recognition in planning seeks to find agent intentions, goals or activities given a set of observations and a knowledge library (e.g. goal states, plans or domain theories). In this work we introduce the problem of Online Action Recognition. It consists in recognizing, in an open world, the planning action that best explains a partially observable state transition from a knowledge library of first-order STRIPS actions, which is initially empty. We frame this as an optimization problem, and propose two algorithms to address it: Action Unification (AU) and Online Action Recognition through Unification (OARU). The former builds on logic unification and generalizes two input actions using weighted partial MaxSAT. The latter looks for an action within the library that explains an observed transition. If there is such action, it generalizes it making use of AU, building in this way an AU hierarchy. Otherwise, OARU inserts a Trivial Grounded Action (TGA) in the library that explains just that transition. We report results on benchmarks from the International Planning Competition and PDDLGym, where OARU recognizes actions accurately with respect to expert knowledge, and shows real-time performance.Peer ReviewedPostprint (author's final draft

Automatic learning of cognitive exercises for socially assistive robotics

© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksIn this paper, we present a learning approach to facilitate the teaching of new board exercises to assistive robotic systems. We formulate the problem as the learning of action models using Boolean predicates, disjunctive preconditions, and existential quantifiers from demonstrations of successful exercise executions. To be able to cope with exercises whose rules depend on a set of features that are initialized at the beginning of each play-out, we introduce the concept of dynamic context. Furthermore, we show how the learnt knowledge can be represented intuitively in a graphical interface that helps the caregiver understand what the system has learnt. As validation, we conducted a user study in which we evaluated whether and to which extent different types of feedback can affect the subjects’ performance while teaching three types of exercises: (1) sorting numbers; (2) arranging letters; and (3) reproducing shapes sequences in reversed order. The results suggest that textual and graphical feedback are beneficial.A. Andriella, C. Torras and A. Suarez-Hern ´ andez were partially funded ´ by the European Union´s Horizon 2020 under ERC Advanced Grant CLOTHILDE (no. 741930), G. Alenya by the EU H2020 research and ìnnovation programme IMAGINE (no. 731761) and J. Segovia-Aguas by the programme TAILOR (no. 952215). The work was partially supported by the Spanish State Research Agency through the María de Maeztu Seal of Excellence to IRI (MDM-2016-0656)Peer ReviewedAward-winningPostprint (author's final draft

Automatic learning of cognitive exercises for socially assistive robotics

© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksIn this paper, we present a learning approach to facilitate the teaching of new board exercises to assistive robotic systems. We formulate the problem as the learning of action models using Boolean predicates, disjunctive preconditions, and existential quantifiers from demonstrations of successful exercise executions. To be able to cope with exercises whose rules depend on a set of features that are initialized at the beginning of each play-out, we introduce the concept of dynamic context. Furthermore, we show how the learnt knowledge can be represented intuitively in a graphical interface that helps the caregiver understand what the system has learnt. As validation, we conducted a user study in which we evaluated whether and to which extent different types of feedback can affect the subjects’ performance while teaching three types of exercises: (1) sorting numbers; (2) arranging letters; and (3) reproducing shapes sequences in reversed order. The results suggest that textual and graphical feedback are beneficial.A. Andriella, C. Torras and A. Suarez-Hern ´ andez were partially funded ´ by the European Union´s Horizon 2020 under ERC Advanced Grant CLOTHILDE (no. 741930), G. Alenya by the EU H2020 research and ìnnovation programme IMAGINE (no. 731761) and J. Segovia-Aguas by the programme TAILOR (no. 952215). The work was partially supported by the Spanish State Research Agency through the María de Maeztu Seal of Excellence to IRI (MDM-2016-0656)Peer ReviewedAward-winningPostprint (author's final draft

Leveraging Multiple Environments for Learning and Decision Making: a Dismantling Use Case

International audienceLearning is usually performed by observing real robot executions. Physics-based simulators are a good alternative for providing highly valuable information while avoiding costly and potentially destructive robot executions. We present a novel approach for learning the probabilities of symbolic robot action outcomes. This is done leveraging different environments, such as physics-based simulators, in execution time. To this end, we propose MENID (Multiple Environment Noise Indeterministic Deictic) rules, a novel representation able to cope with the inherent uncertainties present in robotic tasks. MENID rules explicitly represent each possible outcomes of an action, keep memory of the source of the experience, and maintain the probability of success of each outcome. We also introduce an algorithm to distribute actions among environments, based on previous experiences and expected gain. Before using physicsbased simulations, we propose a methodology for evaluating different simulation settings and determining the least timeconsuming model that could be used while still producing coherent results. We demonstrate the validity of the approach in a dismantling use case, using a simulation with reduced quality as simulated system, and a simulation with full resolution where we add noise to the trajectories and some physical parameters as a representation of the real system

Program synthesis for generalized planning

Generalized planning is the problem of finding an algorithm-like solution called generalized plan to multiple planning instances. The two main tasks to perform in generalized planning are synthesizing and validating generalized plans. In this thesis, we represent generalized plans as a planning programs, enhanced with conditional goto conditions, or finite state controllers. Then, we compile generalized planning problems to PDDL such that we can compute programs using off-the-shelf classical planners. Because solutions to generalized planning are similar to algorithms, we can build libraries of previous knowledge and reuse them if necessary using a call stack. This feature extends to planning programs with procedures, hierarchical finite state controllers and allows recursion. Finally, we introduce new application areas for planning, e.g. unsupervised classification of instances or context-free grammar generation, by defining non-deterministic choice functions for planning programs.La Planificació Generalitzada és el problema de trobar una solució en forma d'algorisme anomenat pla generalitzat a múltiples instàncies de planificació. Les principals tasques són la síntesi i la validació de plans generalizats. En aquesta tesi, representem els plans generalizats com programes de planificació millorats amb salts condicionals, o controladors d'estat finits. Després, compilem problemes de planificació generalitzada a PDDL per poder computar-los amb qualsevol planificador clàssic que estigui llest per utilitzar-se. Com les solucions són algorismes, podem construir llibreries de coneixement previ y reutilitzar-les amb una pila de trucades. Aquesta característica s'estén als programes de planificació amb procediments, als controladors d'estat finits jeràrquics i permet recursivitat. Finalmente, donem a conèixer noves àrees on aplicar planificació, per exemple la classificació no supervisada d'instàncies o la generació de gramàtica lliure de context, gràcies a la definició d'una funció d'elecció no determinista.La Planificación Generalizada es el problema de encontrar una solución en forma de algoritmo llamada plan generalizado a múltiples instancias de planificación. Las principales tareas son la síntesis y la validación de planes generalizados. En esta tesis, representamos los planes generalizados como programas de planificación mejorados con saltos condicionales, o controladores de estado finitos. Después, compilamos problemas de planificación generalizada a PDDL tal que podamos computarlos utilizando cualquier planificador clásico que esté listo para usarse. Como las soluciones son algoritmos, podemos construir librerías de conocimiento previo y reutilizarlas con una pila de llamadas. Esta característica se extiende a los programas de planificación con procedimientos, a los controladores de estado finitos jerárquicos y permite recursividad. Finalmente, damos a conocer nuevas areas dónde aplicar planificación, por ejemplo la clasificación no supervisada de instancias o la generación de gramática libre de contexto, gracias a la definición de una función de elección no determinista