CASP Solutions for Planning in Hybrid Domains

CASP is an extension of ASP that allows for numerical constraints to be added in the rules. PDDL+ is an extension of the PDDL standard language of automated planning for modeling mixed discrete-continuous dynamics. In this paper, we present CASP solutions for dealing with PDDL+ problems, i.e., encoding from PDDL+ to CASP, and extensions to the algorithm of the EZCSP CASP solver in order to solve CASP programs arising from PDDL+ domains. An experimental analysis, performed on well-known linear and non-linear variants of PDDL+ domains, involving various configurations of the EZCSP solver, other CASP solvers, and PDDL+ planners, shows the viability of our solution.Comment: Under consideration in Theory and Practice of Logic Programming (TPLP

On the predictability of domain-independent temporal planners

Temporal planning is a research discipline that addresses the problem of generating a totally or a partially ordered sequence of actions that transform the environment from some initial state to a desired goal state, while taking into account time constraints and actions' duration. For its ability to describe and address temporal constraints, temporal planning is of critical importance for a wide range of real-world applications. Predicting the performance of temporal planners can lead to significant improvements in the area, as planners can then be combined in order to boost the performance on a given set of problem instances. This paper investigates the predictability of the state-of-the-art temporal planners by introducing a new set of temporal-specific features and exploiting them for generating classification and regression empirical performance models (EPMs) of considered planners. EPMs are also tested with regard to their ability to select the most promising planner for efficiently solving a given temporal planning problem. Our extensive empirical analysis indicates that the introduced set of features allows to generate EPMs that can effectively perform algorithm selection, and the use of EPMs is therefore a promising direction for improving the state of the art of temporal planning, hence fostering the use of planning in real-world applications.</p

Logic programming for deliberative robotic task planning

Over the last decade, the use of robots in production and daily life has increased. With increasingly complex tasks and interaction in different environments including humans, robots are required a higher level of autonomy for efficient deliberation. Task planning is a key element of deliberation. It combines elementary operations into a structured plan to satisfy a prescribed goal, given specifications on the robot and the environment. In this manuscript, we present a survey on recent advances in the application of logic programming to the problem of task planning. Logic programming offers several advantages compared to other approaches, including greater expressivity and interpretability which may aid in the development of safe and reliable robots. We analyze different planners and their suitability for specific robotic applications, based on expressivity in domain representation, computational efficiency and software implementation. In this way, we support the robotic designer in choosing the best tool for his application

The IBaCoP planning system: instance-based configured portfolios

Sequential planning portfolios are very powerful in exploiting the complementary strength of different automated planners. The main challenge of a portfolio planner is to define which base planners to run, to assign the running time for each planner and to decide in what order they should be carried out to optimize a planning metric. Portfolio configurations are usually derived empirically from training benchmarks and remain fixed for an evaluation phase. In this work, we create a per-instance configurable portfolio, which is able to adapt itself to every planning task. The proposed system pre-selects a group of candidate planners using a Pareto-dominance filtering approach and then it decides which planners to include and the time assigned according to predictive models. These models estimate whether a base planner will be able to solve the given problem and, if so, how long it will take. We define different portfolio strategies to combine the knowledge generated by the models. The experimental evaluation shows that the resulting portfolios provide an improvement when compared with non-informed strategies. One of the proposed portfolios was the winner of the Sequential Satisficing Track of the International Planning Competition held in 2014.We thank the authors of the base planners because our work is based largely on their previous effort. This work has been partially supported by the Spanish projects TIN2011-27652-C03-02, TIN2012-38079-C03-02 and TIN2014-55637-C2-1-R

Creating planning portfolios with predictive models

Mención Internacional en el título de doctorSequential planning portfolios are very powerful in exploiting the complementary strength of different automated planners: for each planning task there are one or more base planners that obtain the best solution. Therefore, the main challenge when building a planning portfolio is to ensure that a suitable planner be chosen and that it gets enough planning time. To solve this problem we need firstly to define three elements. The first is the settings or planning conditions: time, memory, or other constraints. The second one is the set of base planners. And finally, a benchmark that provides us with knowledge on how the base planners will behave under the given settings, following some kind of inductive process. Ideally, if the previous elements are correctly defined, when a new planning task arrives, an oracle will be able to tell which base planner to run and for how long. In practice, since no oracle exists, the challenge to choose a sub-set of base planners, is assigning them a running time and deciding the order in which they are run to optimize a planning metric under the predefined settings. Many state-of-the- art portfolios might never achieve an optimal performance because they do not select different planners for the different planning tasks. In addition, these static techniques typically assign a fixed running time to the selected set of planners, independently of the task. besides, the old-fashioned dynamic portfolios present a poor characterization of the planning task and do not have enough knowledge to predict an accurate portfolio configuration in many cases. The aforementioned drawbacks are intensified by the fact that there is an increasing number of planners available to choose from, although many of them are designed following similar approaches, so they are expected to behave similarly. This dissertation is built on two main hypotheses. Firstly that the space of the base planners can be reduced just by selecting a subset of diverse or complementary planners; e.g. that there is a minimal set of planners that ensure that the optimal portfolio can be computed. Secondly, that planning tasks can be characterized, and that the difficulty in solving them can be modelled as a function of these features. To evaluate the first hypothesis, we analyze different metrics that could be used to filter the initial set of base planners. Classical metrics such as coverage, quality or execution time have been chosen by different portfolios in the past. We demonstrate that these selection methods may reduce the diversity of the portfolios, and propose an alternative method based on the Pareto dominance. We then carry out a profound analysis on previous planning task characterizations and show how we could exploit them in current planning paradigms. A group of very informative features are proposed to improve the current feature definition of the planning tasks. These features have enough knowledge to differentiate planning tasks with similar \a priori" complexity. In this thesis we demonstrate that the implicit knowledge can be exploited in the construction of predictive models. These models estimate whether a base planner will be able to solve a given problem and, if so, how long it will take. Nevertheless, the predictive models are not perfect and sometimes provide wrong (or inaccurate) predictions. To solve this kind of problems, we propose different portfolio strategies to combine the number of selected base planners and their times. These strategies take into account the predefined settings and the knowledge learned in previous phases. In conclusion, this thesis sets out a profound analysis of three different mechanisms or steps to create planning portfolios with predictive models, including new proposals for developing: planner filtering, planning task featuring, learning predictive models and portfolio construction strategies. One of the proposed portfolios was the winner of the Sequential Satisficing Track of the International Planning Competition held in 2014Los portfolios de planificadores tienen un gran potencial ya que pueden aprovecharse de los diferentes planificadores automáticos, consiguiendo mejorar el rendimiento de un único planificador. Sin embargo, la creación de un portfolio no es una tarea sencilla, ya que para poder crear uno lo suficientemente bueno, hay que tratar tres problemas fundamentales. El primero de ellos es encontrar qué planificadores hay que seleccionar como componentes del mismo. La segunda es el tiempo que hay que asignar a cada planificador y, la última y no menos importante el orden en el que se tienen que ejecutar. Actualmente en el estado del arte, estas configuraciones, se realizan a partir de los resultados obtenidos por los planificadores en una fase previa de entrenamiento con un conjunto de problemas y restricciones prefijado (tiempo, memoria, etc), consiguiendo una configuración específica tratando de optimizar una métrica. Idealmente, la mejor configuración posible consiste en asignar el tiempo suficiente al mejor planificador para cada tarea de planificación. Sin embargo, esta configuración no siempre es posible, y hay que recurrir a otras aproximaciones como asignar un tiempo fijo a una selección de planificadores. Ésta no es la única simplificación utilizada, existen otras técnicas más cercanas a la óptima, en las cuales se selecciona un planificador o varios en función de la tarea a resolver. Sin embargo, estos sistemas, denominados dinámicos, incluyen una escasa caracterización de las tareas de planificación. En esta tesis se parte de dos hipótesis. La primera de ellas es que existe un conjunto reducido de planificadores que maximiza la diversidad. La segunda de ellas consiste en la posibilidad de crear un conjunto de descriptivos lo suficientemente bueno para caracterizar la tarea de planificación. La caracterización de las tareas de planificación puede estar basada en sus distintas representaciones, así como en sus paradigmas. La primera tarea es seleccionar un conjunto de planificadores; realizando un análisis basado en las métricas clásicas de planificación, como son problemas resueltos, calidad y tiempo para seleccionar un subconjunto de planificadores. Adicionalmente, proponemos como alternativa a estas métricas, una técnica multiobjetivo. Este criterio está basado en la dominancia de Pareto combinando las métricas de tiempo y calidad. Continuando con nuestras hip_otesis es necesario crear un conjunto de características bien informado para la tarea de planificación. Estas características deben ser capaces de diferenciar adecuadamente por problema y para ello sería necesario basarse en los distintos paradigmas de la planificación automática. Este grupo de características tienen que ser úutiles para crear modelos predictivos. Estos modelos podrán darnos además de una selección de planificadores, una aproximación del tiempo asignado a cada componente y el orden de los mismos. Adicionalmente se presentarán una serie de estrategias para explotar el conocimiento obtenido con los modelos predictivos. En conclusión, se plantea y desarrolla un sistema para configurar porfolios de planificadores usando modelos predictivos en tres fases distintas. Una instanciación de este sistema fue el ganador de la competición internacional de planificación en el áarea de satisfacibilidad en el año 2014.Programa Oficial de Doctorado en Ciencia y Tecnología InformáticaPresidente: María Araceli Sanchís de Miguel.- Secretario: Álvaro Torralba Arias de Reyna.- Vocal: Alessandro Saett

Grounding Classical Task Planners via Vision-Language Models

Classical planning systems have shown great advances in utilizing rule-based human knowledge to compute accurate plans for service robots, but they face challenges due to the strong assumptions of perfect perception and action executions. To tackle these challenges, one solution is to connect the symbolic states and actions generated by classical planners to the robot's sensory observations, thus closing the perception-action loop. This research proposes a visually-grounded planning framework, named TPVQA, which leverages Vision-Language Models (VLMs) to detect action failures and verify action affordances towards enabling successful plan execution. Results from quantitative experiments show that TPVQA surpasses competitive baselines from previous studies in task completion rate

The GRT Planning System: Backward Heuristic Construction in Forward State-Space Planning

This paper presents GRT, a domain-independent heuristic planning system for STRIPS worlds. GRT solves problems in two phases. In the pre-processing phase, it estimates the distance between each fact and the goals of the problem, in a backward direction. Then, in the search phase, these estimates are used in order to further estimate the distance between each intermediate state and the goals, guiding so the search process in a forward direction and on a best-first basis. The paper presents the benefits from the adoption of opposite directions between the preprocessing and the search phases, discusses some difficulties that arise in the pre-processing phase and introduces techniques to cope with them. Moreover, it presents several methods of improving the efficiency of the heuristic, by enriching the representation and by reducing the size of the problem. Finally, a method of overcoming local optimal states, based on domain axioms, is proposed. According to it, difficult problems are decomposed into easier sub-problems that have to be solved sequentially. The performance results from various domains, including those of the recent planning competitions, show that GRT is among the fastest planners

Interpretable task planning and learning for autonomous robotic surgery with logic programming

This thesis addresses the long-term goal of full (supervised) autonomy in surgery, characterized by dynamic environmental (anatomical) conditions, unpredictable workflow of execution and workspace constraints. The scope is to reach autonomy at the level of sub-tasks of a surgical procedure, i.e. repetitive, yet tedious operations (e.g., dexterous manipulation of small objects in a constrained environment, as needle and wire for suturing). This will help reducing time of execution, hospital costs and fatigue of surgeons during the whole procedure, while further improving the recovery time for the patients. A novel framework for autonomous surgical task execution is presented in the first part of this thesis, based on answer set programming (ASP), a logic programming paradigm, for task planning (i.e., coordination of elementary actions and motions). Logic programming allows to directly encode surgical task knowledge, representing emph{plan reasoning methodology} rather than a set of pre-defined plans. This solution introduces several key advantages, as reliable human-like interpretable plan generation, real-time monitoring of the environment and the workflow for ready adaptation and failure recovery. Moreover, an extended review of logic programming for robotics is presented, motivating the choice of ASP for surgery and providing an useful guide for robotic designers. In the second part of the thesis, a novel framework based on inductive logic programming (ILP) is presented for surgical task knowledge learning and refinement. ILP guarantees fast learning from very few examples, a common drawback of surgery. Also, a novel action identification algorithm is proposed based on automatic environmental feature extraction from videos, dealing for the first time with small and noisy datasets collecting different workflows of executions under environmental variations. This allows to define a systematic methodology for unsupervised ILP. All the results in this thesis are validated on a non-standard version of the benchmark training ring transfer task for surgeons, which mimics some of the challenges of real surgery, e.g. constrained bimanual motion in small space