SHOP2: An HTN Planning System

The SHOP2 planning system received one of the awards for distinguished performance in the 2002 International Planning Competition. This paper describes the features of SHOP2 which enabled it to excel in the competition, especially those aspects of SHOP2 that deal with temporal and metric planning domains

SHOP2: An HTN planning system

The SHOP2 planning system received one of the awards for distinguished performance in the 2002 International Planning Competition. This paper describes the features of SHOP2 which enabled it to excel in the competition, especially those aspects of SHOP2 that deal with temporal and metric planning domains.open17833

Architecture for planning and execution of missions with fleets of unmanned vehicles

Esta tesis presenta contribuciones en el campo de la planificación automática y la programación de tareas, la rama de la inteligencia artificial que se ocupa de la realización de estrategias o secuencias de acciones típicamente para su ejecución por parte de vehículos no tripulados, robots autónomos y/o agentes inteligentes. Cuando se intenta alcanzar un objetivo determinado, la cooperación puede ser un aspecto clave. La complejidad de algunas tareas requiere la cooperación entre varios agentes. Mas aún, incluso si una tarea es lo suficientemente simple para ser llevada a cabo por un único agente, puede usarse la cooperación para reducir el coste total de la misma. Para realizar tareas complejas que requieren interacción física con el mundo real, los vehículos no tripulados pueden ser usados como agentes. En los últimos años se han creado y utilizado una gran diversidad de plataformas no tripuladas, principalmente vehículos que pueden ser dirigidos sin un humano a bordo, tanto en misiones civiles como militares. En esta tesis se aborda la aplicación de planificación simbólica de redes jerárquicas de tareas (HTN planning, por sus siglas en inglés) en la resolución de problemas de enrutamiento de vehículos (VRP, por sus siglas en inglés) [18], en dominios que implican múltiples vehículos no tripulados de capacidades heterogéneas que deben cooperar para alcanzar una serie de objetivos específicos. La planificación con redes jerárquicas de tareas describe dominios utilizando una descripción que descompone conjuntos de tareas en subconjuntos más pequeños de subtareas gradualmente, hasta obtener tareas del más bajo nivel que no pueden ser descompuestas y se consideran directamente ejecutables. Esta jerarquía es similar al modo en que los humanos razonan sobre los problemas, descomponiéndolos en subproblemas según el contexto, y por lo tanto suelen ser fáciles de comprender y diseñar. Los problemas de enrutamiento de vehículos son una generalización del problema del viajante (TSP, por sus siglas en inglés). La resolución del problema del viajante consiste en encontrar la ruta más corta posible que permite visitar una lista de ciudades, partiendo y acabando en la misma ciudad. Su generalización, el problema de enrutamiento de vehículos, consiste en encontrar el conjunto de rutas de longitud mínima que permite cubrir todas las ciudades con un determinado número de vehículos. Ambos problemas cuentan con una fuerte componente combinatoria para su resolución, especialmente en el caso del VRP, por lo que su presencia en dominios que van a ser tratados con un planificador HTN clásico supone un gran reto. Para la aplicación de un planificador HTN en la resolución de problemas de enrutamiento de vehículos desarrollamos dos métodos. En el primero de ellos presentamos un sistema de optimización de soluciones basado en puntuaciones, que nos permite una nueva forma de conexión entre un software especializado en la resolución del VRP con el planificador HTN. Llamamos a este modo de conexión el método desacoplado, puesto que resolvemos la componente combinatoria del problema de enrutamiento de vehículos mediante un solucionador específico que se comunica con el planificador HTN y le suministra la información necesaria para continuar con la descomposición de tareas. El segundo método consiste en mejorar el planificador HTN utilizado para que sea capaz de resolver el problema de enrutamiento de vehículos de la mejor forma posible sin tener que depender de módulos de software externos. Llamamos a este modo el método acoplado. Con este motivo hemos desarrollado un nuevo planificador HTN que utiliza un algoritmo de búsqueda distinto del que se utiliza normalmente en planificadores de este tipo. Esta tesis presenta nuevas contribuciones en el campo de la planificación con redes jerárquicas de tareas para la resolución de problemas de enrutamiento de vehículos. Se aplica una nueva forma de conexión entre dos planificadores independientes basada en un sistema de cálculo de puntuaciones que les permite colaborar en la optimización de soluciones, y se presenta un nuevo planificador HTN con un algoritmo de búsqueda distinto al comúnmente utilizado. Se muestra la aplicación de estos dos métodos en misiones civiles dentro del entorno de los Proyectos ARCAS y AEROARMS financiados por la Comisión Europea y se presentan extensos resultados de simulación para comprobar la validez de los dos métodos propuestos.This thesis presents contributions in the field of automated planning and scheduling, the branch of artificial intelligence that concerns the realization of strategies or action sequences typically for execution by unmanned vehicles, autonomous robots and/or intelligent agents. When trying to achieve certain goal, cooperation may be a key aspect. The complexity of some tasks requires the cooperation among several agents. Moreover, even if the task is simple enough to be carried out by a single agent, cooperation can be used to decrease the overall cost of the operation. To perform complex tasks that require physical interaction with the real world, unmanned vehicles can be used as agents. In the last years a great variety of unmanned platforms, mainly vehicles that can be driven without a human on board, have been developed and used both in civil and military missions. This thesis deals with the application of Hierarchical Task Network (HTN) planning in the resolution of vehicle routing problems (VRP) [18] in domains involving multiple heterogeneous unmanned vehicles that must cooperate to achieve specific goals. HTN planning describes problem domains using a description that decomposes set of tasks into subsets of smaller tasks and so on, obtaining low-level tasks that cannot be further decomposed and are supposed to be executable. The hierarchy resembles the way the humans reason about problems by decomposing them into sub-problems depending on the context and therefore tend to be easy to understand and design. Vehicle routing problems are a generalization of the travelling salesman problem (TSP). The TSP consists on finding the shortest path that connects all the cities from a list, starting and ending on the same city. The VRP consists on finding the set of minimal routes that cover all cities by using a specific number of vehicles. Both problems have a combinatorial nature, specially the VRP, that makes it very difficult to use a HTN planner in domains where these problems are present. Two approaches to use a HTN planner in domains involving the VRP have been tested. The first approach consists on a score-based optimization system that allows us to apply a new way of connecting a software specialized in the resolution of the VRP with the HTN planner. We call this the decoupled approach, as we tackle the combinatorial nature of the VRP by using a specialized solver that communicates with the HTN planner and provides all the required information to do the task decomposition. The second approach consists on improving and enhancing the HTN planner to be capable of solving the VRP without needing the use of an external software. We call this the coupled approach. For this reason, a new HTN planner that uses a different search algorithm from these commonly used in that type of planners has been developed and is presented in this work. This thesis presents new contributions in the field of hierarchical task network planning for the resolution of vehicle routing problem domains. A new way of connecting two independent planning systems based on a score calculation system that lets them cooperate in the optimization of the solutions is applied, and a new HTN planner that uses a different search algorithm from that usually used in other HTN planners is presented. These two methods are applied in civil missions in the framework of the ARCAS and AEROARMS Projects funded by the European Commission. Extensive simulation results are presented to test the validity of the two approaches

Raffinement des intentions

Le résumé en français n'a pas été communiqué par l'auteur.Le résumé en anglais n'a pas été communiqué par l'auteur

Personalized conciliation of clinical guidelines for comorbid patients through multi-agent planning

[EN] The conciliation of multiple single-disease guidelines for comorbid patients entails solving potential clinical interactions, discovering synergies in the diagnosis and the recommendations, and managing clinical equipoise situations. Personalized conciliation of multiple guidelines considering additionally patient preferences brings some further difficulties. Recently, several works have explored distinct techniques to come up with an automated process for the conciliation of clinical guidelines for comorbid patients but very little attention has been put in integrating the patient preferences into this process. In this work, a Multi-Agent Planning (MAP) framework that extends previous work on single-disease temporal Hierarchical Task Networks (HTN) is proposed for the automated conciliation of clinical guidelines with patient-centered preferences. Each agent encapsulates a single-disease Computer Interpretable Guideline (CIG) formalized as an HTN domain and conciliates the decision procedures that encode the clinical recommendations of its CIG with the decision procedures of the other agents' CIGs. During conciliation, drug-related interactions, scheduling constraints as well as redundant actions and multiple support interactions are solved by an automated planning process. Moreover, the simultaneous application of the patient preferences in multiple diseases may potentially bring about contradictory clinical decisions and more interactions. As a final step, the most adequate personalized treatment plan according to the patient preferences is selected by a Multi-Criteria Decision Making (MCDM) process. The MAP approach is tested on a case study that builds upon a simplified representation of two real clinical guidelines for Diabetes Mellitus and Arterial Hypertension.This work has been partially supported by Spanish Government Projects MINECO TIN2014-55637-C2-2-R and TIN2015-71618-R.Fernández-Olivares, J.; Onaindia De La Rivaherrera, E.; Castillo Vidal, L.; Jordán, J.; Cózar, J. (2019). Personalized conciliation of clinical guidelines for comorbid patients through multi-agent planning. Artificial Intelligence in Medicine. 96:167-186. https://doi.org/10.1016/j.artmed.2018.11.003S1671869

Integration of social aspects in a multi-agent platform running in a supercomputer

El modelado basado en agentes es una de las formas más apropiadas para simular y analizar problemas y simulaciones complejas, como la simulación de entornos y escenarios sociales. El tipo de plataforma que más se utiliza en estas tareas es la de un sistema multiagente. Los sistemas multiagente se componen de varios actores (agentes) en un entorno de simulación concreto, y cada uno de ellos posee un conocimiento y un comportamiento individual. Estos sistemas pueden utilizarse para analizar el comportamiento emergente colectivo en contextos como la sociología, la economía, la elaboración de políticas sociales y económicas, etc. Las plataformas multiagente actuales o bien escalan bastante bien en computación pero implementan mecanismos de razonamiento muy simples, o bien emplean sistemas de razonamiento complejos a costa de escalabilidad. En un trabajo reciente realizado en la UPC, se ha propuesto, teorizado e implementado una plataforma que permite escalar y ejecutar paralelamente agentes complejos con planificación HTN. Este proyecto amplía dicha plataforma para permitir un mejor análisis de las relaciones sociales entre los agentes mediante las preferencias sobre sus objetivos, las preferencias sobre sus planes, sus acciones y valores morales, a la vez que nos aseguramos de que nuestras adiciones sean escalables, para mantener el espíritu y el propósito de la plataforma. En este trabajo, partimos del trabajo previo realizado por Dmitry Gnatyshak sobre la implementación de dicha plataforma, y lo ampliamos, tanto formalmente como a nivel de implementación. Formalizamos las ampliaciones del modelo del sistema, así como sus modificaciones, y hacemos lo mismo con la implementación. Al final, proporcionamos un complejo escenario de ejemplo para mostrar todas las ampliaciones que hemos creado y/o añadido.Agent-based modeling is one of the most suitable ways to simulate and analyze complex problems and simulations, such as the simulation of societal environments and scenarios. The kind of platform most commonly used in these endeavors is that of a multi-agent system. Multi-agent systems are comprised of various actors (agents) in a concrete simulation environment, each of them possessing an individual knowledge and an individual behavior. These systems can be used to analyze collective emergent behavior in contexts such as sociology, economics, policy making, etc. Current Multi-agent platforms either scale in computation quite well but implement very simple reasoning mechanisms, or employ complex reasoning systems at the expense of scalability. In recent work done at UPC, a platform enabling complex agents with HTN planning to scale and run parallelly was proposed, theorized, and implemented. This project extends said platform to enable a better analysis of the social relationships between agents by means of preferences over their objectives, preferences over their plans, actions, and moral values, while making sure our additions are scalable, to maintain the spirit and purpose of the platform. In this work, we start from the previous work done by Dmitry Gnatyshak on implementing said platform, and we expand it, both formally and imple- mentation-wise. We formalize the additions to the model of the system, as well as its modifications, and we do the same for the implementation. In the end, we provide a complex example scenario to showcase all the additions we have created

Machine learning in hybrid hierarchical and partial-order planners for manufacturing domains

The application of AI planning techniques to manufacturing Systems is being widely deployed for all the tasks involved in the process, from product design to production planning and control. One of these problems is the automatic generation of control sequences for the entire manufacturing system in such a way that final plans can be directly use das the sequential control programs which drive the operation of manufacturing systems. Hybis is a hierarchical and nonlinear planner whose goal is to obtain partially ordered plans at such a level of detail that they can be use das sequential control programs for manufacturing systems. Currently, those sequential control programs are being generated by hand using modelling tools. This document describes a work whose aim is to improve the efficiency of solving problems with Hybis by using machine learning techniques. It implements a deductive learning method that is able to automatically acquire control knowledge (heuristics) by generating bounded explanations of the problem solving episodes. The learning approach builds on Hamlet, a system that learns control knowledge in the form of control rules.This work was partially supported by a grant from the Ministerio de Ciencia y Tecnología through projects TAP1999-0535-C02-02, TIC2001-4936-E, and TIC2002-04146-C05-05.Publicad