ARBEITSBEREICH WISSENSBASIERTE SYSTEME TEAM PROGRAMMING IN GOLOG UNDER PARTIAL OBSERVABILITY

Abstract. We present and explore the agent programming language TEAMGOLOG, which is a novel approach to programming a team of cooperative agents under partial observability. Every agent is associated with a partial control program in Golog, which is completed by the TEAMGOLOG interpreter in an optimal way by assuming a decision-theoretic semantics. The approach is based on the key concepts of a synchronization state and a communication state, which allow the agents to passively resp. actively coordinate their behavior, while keeping their belief states, observations, and activities invisible to the other agents. We show the practical usefulness of the TEAMGOLOG approach in a rescue simulated domain. We describe the algorithms behind the TEAMGOLOG interpreter and provide a prototype implementation. We also show through experimental results that the TEAMGOLOG approach outperforms a standard greedy one in the rescue simulated domain

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

An evaluation of user acceptance of a corporate intranet

Intranets represent an important organisational resource for knowledge sharing. However, as yet, there has been little research into the quality of intranets and the impact of quality on intranet user acceptance. In the study reported in this paper, an intranet quality assessment tool comprising the dimensions usability, design, and information quality, is combined with perceived usefulness and social influence from the technology acceptance literature to create an intranet acceptance model. The model is applied to the sales and marketing division of an international manufacturing company. Data is collected via a Web survey (n=131, response rate = 65.5%) and tested using the partial least squares approach to structural equation modelling. The results show that intranet quality is a significant factor in determining behavioural intention to use, although it is less important than perceived usefulness and social influence. Comments collected from respondents are used to illustrate the findings and provide an insight into user behaviour. The discussion considers the implications, future research (e.g., the role of social influence in intranet usage) and limitations. The paper rounds off with a short summary

Multi-robot coordination using flexible setplays : applications in RoboCup's simulation and middle-size leagues

Tese de Doutoramento. Engenharia Informática. Faculdade de Engenharia. Universidade do Porto. 201

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

Situationsverstehen für die Risikobeurteilung bei der Mensch-Roboter-Kooperation

In dem vorgestellten System wird die Umgebung eines Industrieroboters mittels Algorithmen des maschinellen Lernens erfasst und Objekte sowie menschliche Handlungen bestimmt. Anhand semantischer Analyse kann auf vorliegende Situationen geschlossen werden, wodurch sich dynamisch Risikobewertungen und Handlungsvorgaben für den Roboter ableiten lassen. Diese stellen die Grundlage für ein reaktives Roboterverhalten dar, das eine zielgerichtete und sichere Mensch-Roboter-Kooperation ermöglicht

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