Storing and Indexing Plan Derivations through Explanation-based Analysis of Retrieval Failures

Case-Based Planning (CBP) provides a way of scaling up domain-independent planning to solve large problems in complex domains. It replaces the detailed and lengthy search for a solution with the retrieval and adaptation of previous planning experiences. In general, CBP has been demonstrated to improve performance over generative (from-scratch) planning. However, the performance improvements it provides are dependent on adequate judgements as to problem similarity. In particular, although CBP may substantially reduce planning effort overall, it is subject to a mis-retrieval problem. The success of CBP depends on these retrieval errors being relatively rare. This paper describes the design and implementation of a replay framework for the case-based planner DERSNLP+EBL. DERSNLP+EBL extends current CBP methodology by incorporating explanation-based learning techniques that allow it to explain and learn from the retrieval failures it encounters. These techniques are used to refine judgements about case similarity in response to feedback when a wrong decision has been made. The same failure analysis is used in building the case library, through the addition of repairing cases. Large problems are split and stored as single goal subproblems. Multi-goal problems are stored only when these smaller cases fail to be merged into a full solution. An empirical evaluation of this approach demonstrates the advantage of learning from experienced retrieval failure.Comment: See http://www.jair.org/ for any accompanying file

A Domain-Independent Algorithm for Plan Adaptation

The paradigms of transformational planning, case-based planning, and plan debugging all involve a process known as plan adaptation - modifying or repairing an old plan so it solves a new problem. In this paper we provide a domain-independent algorithm for plan adaptation, demonstrate that it is sound, complete, and systematic, and compare it to other adaptation algorithms in the literature. Our approach is based on a view of planning as searching a graph of partial plans. Generative planning starts at the graph's root and moves from node to node using plan-refinement operators. In planning by adaptation, a library plan - an arbitrary node in the plan graph - is the starting point for the search, and the plan-adaptation algorithm can apply both the same refinement operators available to a generative planner and can also retract constraints and steps from the plan. Our algorithm's completeness ensures that the adaptation algorithm will eventually search the entire graph and its systematicity ensures that it will do so without redundantly searching any parts of the graph.Comment: See http://www.jair.org/ for any accompanying file

J-MOD2^{2}: Joint Monocular Obstacle Detection and Depth Estimation

In this work, we propose an end-to-end deep architecture that jointly learns to detect obstacles and estimate their depth for MAV flight applications. Most of the existing approaches either rely on Visual SLAM systems or on depth estimation models to build 3D maps and detect obstacles. However, for the task of avoiding obstacles this level of complexity is not required. Recent works have proposed multi task architectures to both perform scene understanding and depth estimation. We follow their track and propose a specific architecture to jointly estimate depth and obstacles, without the need to compute a global map, but maintaining compatibility with a global SLAM system if needed. The network architecture is devised to exploit the joint information of the obstacle detection task, that produces more reliable bounding boxes, with the depth estimation one, increasing the robustness of both to scenario changes. We call this architecture J-MOD$^{2}$. We test the effectiveness of our approach with experiments on sequences with different appearance and focal lengths and compare it to SotA multi task methods that jointly perform semantic segmentation and depth estimation. In addition, we show the integration in a full system using a set of simulated navigation experiments where a MAV explores an unknown scenario and plans safe trajectories by using our detection model

The 1990 progress report and future plans

This document describes the progress and plans of the Artificial Intelligence Research Branch (RIA) at ARC in 1990. Activities span a range from basic scientific research to engineering development and to fielded NASA applications, particularly those applications that are enabled by basic research carried out at RIA. Work is conducted in-house and through collaborative partners in academia and industry. Our major focus is on a limited number of research themes with a dual commitment to technical excellence and proven applicability to NASA short, medium, and long-term problems. RIA acts as the Agency's lead organization for research aspects of artificial intelligence, working closely with a second research laboratory at JPL and AI applications groups at all NASA centers

Impliance: A Next Generation Information Management Appliance

ably successful in building a large market and adapting to the changes of the last three decades, its impact on the broader market of information management is surprisingly limited. If we were to design an information management system from scratch, based upon today's requirements and hardware capabilities, would it look anything like today's database systems?" In this paper, we introduce Impliance, a next-generation information management system consisting of hardware and software components integrated to form an easy-to-administer appliance that can store, retrieve, and analyze all types of structured, semi-structured, and unstructured information. We first summarize the trends that will shape information management for the foreseeable future. Those trends imply three major requirements for Impliance: (1) to be able to store, manage, and uniformly query all data, not just structured records; (2) to be able to scale out as the volume of this data grows; and (3) to be simple and robust in operation. We then describe four key ideas that are uniquely combined in Impliance to address these requirements, namely the ideas of: (a) integrating software and off-the-shelf hardware into a generic information appliance; (b) automatically discovering, organizing, and managing all data - unstructured as well as structured - in a uniform way; (c) achieving scale-out by exploiting simple, massive parallel processing, and (d) virtualizing compute and storage resources to unify, simplify, and streamline the management of Impliance. Impliance is an ambitious, long-term effort to define simpler, more robust, and more scalable information systems for tomorrow's enterprises.Comment: This article is published under a Creative Commons License Agreement (http://creativecommons.org/licenses/by/2.5/.) You may copy, distribute, display, and perform the work, make derivative works and make commercial use of the work, but, you must attribute the work to the author and CIDR 2007. 3rd Biennial Conference on Innovative Data Systems Research (CIDR) January 710, 2007, Asilomar, California, US

Reactive plan execution in multi-agent environments

[ES] Uno de los desafı́os de la robótica es desarrollar sistemas de control capaces de obtener rápidamente respuestas adecuadas e inteligentes para los cambios constantes que tienen lugar en entornos dinámicos. Esta respuesta debe ofrecerse almomento con el objetivo de reanudar la ejecución del plan siempre que se produzca un fallo en el mismo.El término planificación reactiva aborda todos los mecanismos que, directa o indirectamente, promueven la resolución de fallos durante la ejecución del plan. Los sistemas de planificación reactiva funcionan bajo un enfoque de planificación y ejecución continua, es decir, se intercala planificación y ejecución en entornos dinámicos. Muchas de las investigaciones actuales se centran en desarrollar planificadores reactivos que trabajan en escenarios de un único agente para recuperarse rápidamente de los fallos producidos durante la ejecución del plan, pero, si esto no es posible, pueden requerirse arquitecturas de múltiples agentes y métodos de recuperación más complejos donde varios agentes puedan participar para solucionar el fallo. Por lo tanto, los sistemas de planificación y ejecución continua generalmente generan soluciones para un solo agente. La complejidad de establecer comunicaciones entre los agentes en entornos dinámicos y con restricciones de tiempo ha desanimado a los investigadores a implementar soluciones reactivas donde colaboren varios agentes. En línea con esta investigación, la presente tesis doctoral intenta superar esta brecha y presenta un modelo de ejecución y planificación reactiva multiagente que realiza un seguimiento de la ejecución de un agente para reparar los fallos con ayuda de otros agentes. En primer lugar, proponemos una arquitectura que comprende un modelo general reactivo de planificación y ejecución que otorga a un agente capacidades de monitorización y ejecución. El modelo también incorpora un planificador reactivo que proporciona al agente respuestas rápidas para recuperarse de los fallos que se pueden producir durante la ejecución del plan. Por lo tanto, la misión de un agente de ejecución es monitorizar, ejecutar y reparar un plan, si ocurre un fallo durante su ejecución. El planificador reactivo está construido sobre un proceso de busqueda limitada en el tiempo que busca soluciones de recuperación para posibles fallos que pueden ocurrir. El agente genera los espacios de búsqueda en tiempo de ejecución con una construcción iterativa limitada en el tiempo que garantiza que el modelo siempre tendrá un espacio de búsqueda disponible para atender un fallo inmediato del plan. Por lo tanto, la única operación que debe hacerse es buscar en el espacio de búsqueda hasta que se encuentre una solución de recuperación. Evaluamos el rendimiento y la reactividad de nuestro planificador reactivo mediante la realización de dos experimentos. Evaluamos la reactividad del planificador para construir espacios de búsqueda dentro de un tiempo disponible dado, asi como támbien, evaluamos el rendimiento y calidad de encontrar soluciones con otros dos métodos deliberativos de planificación. Luego de las investigaciones de un solo agente, propusimos extender el modelo a un contexto de múltiples agentes para la reparación colaborativa donde al menos dos agentes participan en la solución final. El objetivo era idear un modelo de ejecución y planificación reactiva multiagente que garantice el flujo continuo e ininterrumpido de los agentes de ejecución. El modelo reactivo multiagente proporciona un mecanismo de colaboración para reparar una tarea cuando un agente no puede reparar la falla por sí mismo. Para evaluar nuestro sistema, diseñamos diferentes situaciones en tres dominios de planificación del mundo real. Finalmente, el documento presenta algunas conclusiones y también propone futuras lı́neas de investigación posibles.[CA] Un dels desafiaments de la robòtica és desenvolupar sistemes de control capaços d'obtindre ràpidament respostes adequades i intel·ligents per als canvis constants que tenen lloc en entorns dinàmics. Aquesta resposta ha d'oferir-se al moment amb l'objectiu de reprendre l'execució del pla sempre que es produı̈sca una fallada en aquest. El terme planificació reactiva aborda tots els mecanismes que, directa o indirectament, promouen la resolució de fallades durant l'execució del pla. Els sistemes de planificació reactiva funcionen sota un enfocament de planificació i execució contı́nua, és a dir, s'intercala planificació i execució en entorns dinàmics. Moltes de les investigacions actuals se centren en desenvolupar planificadors reactius que treballen en escenaris d'un únic agent per a recuperar-se ràpidament de les fallades produı̈des durant l'execució del pla, però, si això no és possible, poden requerir-se arquitectures de múltiples agents i mètodes de recuperació més complexos on diversos agents puguen participar per a solucionar la fallada. Per tant, els sistemes de planificació i execució contı́nua generalment generen solucions per a un sol agent. La complexitat d'establir comunicacions entre els agents en entorns dinàmics i amb restriccions de temps ha desanimat als investigadors a implementar solucions reactives on col·laboren diversos agents. En lı́nia amb aquesta investigació, la present tesi doctoral intenta superar aquesta bretxa i presenta un model d'execució i planificació reactiva multiagent que realitza un seguiment de l'execució d'un agent per a reparar les fallades amb ajuda d'altres agents. En primer lloc, proposem una arquitectura que comprén un model general reactiu de planificació i execució que atorga a un agent capacitats de monitoratge i execució. El model també incorpora un planificador reactiu que proporciona a l'agent respostes ràpides per a recuperar-se de les fallades que es poden produir durant l'execució del pla. Per tant, la missió d'un agent d'execució és monitorar, executar i reparar un pla, si ocorre una fallada durant la seua execució. El planificador reactiu està construı̈t sobre un procés de cerca limitada en el temps que busca solucions de recuperació per a possibles fallades que poden ocórrer. L'agent genera els espais de cerca en temps d'execució amb una construcció iterativa limitada en el temps que garanteix que el model sempre tindrà un espai de cerca disponible per a atendre una fallada immediata del pla. Per tant, l'única operació que ha de fer-se és buscar en l'espai de cerca fins que es trobe una solució de recuperació. Avaluem el rendiment i la reactivitat del nostre planificador reactiu mitjançant la realització de dos experiments. Avaluem la reactivitat del planificador per a construir espais de cerca dins d'un temps disponible donat, aixı́ com també, avaluem el rendiment i qualitat de trobar solucions amb altres dos mètodes deliberatius de planificació. Després de les investigacions d'un sol agent, vam proposar estendre el model a un context de múltiples agents per a la reparació col·laborativa on almenys dos agents participen en la solució final. L'objectiu era idear un model d'execució i planificació reactiva multiagent que garantisca el flux continu i ininterromput dels agents d'execució. El model reactiu multiagent proporciona un mecanisme de col·laboració per a reparar una tasca quan un agent no pot reparar la falla per si mateix. Explota les capacitats de planificació reactiva dels agents en temps d'execució per a trobar una solució en la qual dos agents participen junts, evitant aixı́ que els agents hagen de recórrer a mecanismes deliberatius. Per a avaluar el nostre sistema, dissenyem diferents situacions en tres dominis de planificació del món real. Finalment, el document presenta algunes conclusions i tam[EN] One of the challenges of robotics is to develop control systems capable of quickly obtaining intelligent, suitable responses for the regularly changing that take place in dynamic environments. This response should be offered at runtime with the aim of resume the plan execution whenever a failure occurs. The term reactive planning addresses all the mechanisms that, directly or indirectly, promote the resolution of failures during the plan execution. Reactive planning systems work under a continual planning and execution approach, i.e., interleaving planning and execution in dynamic environments. Most of the current research puts the focus on developing reactive planning system that works on single-agent scenarios to recover quickly plan failures, but, if this is not possible, we may require more complex multi-agent architectures where several agents may participate to solve the failures. Therefore, continual planning and execution systems have usually conceived solutions for individual agents. The complexity of establishing agent communications in dynamic and time-restricted environments has discouraged researchers from implementing multi-agent collaborative reactive solutions. In line with this research, this Ph.D. dissertation attempts to overcome this gap and presents a multi-agent reactive planning and execution model that keeps track of the execution of an agent to recover from incoming failures. Firstly, we propose an architecture that comprises a general reactive planning and execution model that endows a single-agent with monitoring and execution capabilities. The model also comprises a reactive planner module that provides the agent with fast responsiveness to recover from plan failures. Thus, the mission of an execution agent is to monitor, execute and repair a plan, if a failure occurs during the plan execution. The reactive planner builds on a time-bounded search process that seeks a recovery plan in a solution space that encodes potential fixes for a failure. The agent generates the search space at runtime with an iterative time-bounded construction that guarantees that a solution space will always be available for attending an immediate plan failure. Thus, the only operation that needs to be done when a failure occurs is to search over the solution space until a recovery path is found. We evaluated theperformance and reactiveness of our single-agent reactive planner by conducting two experiments. We have evaluated the reactiveness of the single-agent reactive planner when building solution spaces within a given time limit as well as the performance and quality of the found solutions when compared with two deliberative planning methods. Following the investigations for the single-agent scenario, our proposal is to extend the single model to a multi-agent context for collaborative repair where at least two agents participate in the final solution. The aim is to come up with a multi-agent reactive planning and execution model that ensures the continuous and uninterruptedly flow of the execution agents. The multi-agent reactive model provides a collaborative mechanism for repairing a task when an agent is not able to repair the failure by itself. It exploits the reactive planning capabilities of the agents at runtime to come up with a solution in which two agents participate together, thus preventing agents from having to resort to a deliberative solution. Throughout the thesis document, we motivate the application of the proposed model to the control of autonomous space vehicles in a Planetary Mars scenario. To evaluate our system, we designed different problem situations from three real-world planning domains. Finally, the document presents some conclusions and also outlines future research directions.Gúzman Álvarez, CA. (2019). Reactive plan execution in multi-agent environments [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/12045

SciTech News Volume 71, No. 3 (2017)

Columns and Reports From the Editor.........................3 Division News Science-Technology Division....5 Chemistry Division....................8 Conference Report, Marion E, Sparks Professional Development Award Recipient..9 Engineering Division................10 Engineering Division Award, Winners Reflect on their Conference Experience..15 Aerospace Section of the Engineering Division .....18 Architecture, Building Engineering, Construction, and Design Section of the Engineering Division................20 Reviews Sci-Tech Book News Reviews...22 Advertisements IEEE..........................................

PERFORMANCE EVALUATION AND REVIEW FRAMEWORK OF ROBOTIC MISSIONS (PERFORM): AUTONOMOUS PATH PLANNING AND AUTONOMY PERFORMANCE EVALUATION

The scope of this work spans two main areas of autonomy research 1) autonomous path planning and 2) test and evaluation of autonomous systems. Path planning is an integral part of autonomous decision-making, and a deep understanding in this area provides valuable perspective on approaching the problem of how to effectively evaluate vehicle behavior. Autonomous decision-making capabilities must include reliability, robustness, and trustworthiness in a real-world environment. A major component of robot decision-making lies in intelligent path-planning. Serving as the brains of an autonomous system, an efficient and reliable path planner is crucial to mission success and overall safety. A hybrid global and local planner is implemented using a combination of the Potential Field Method (PFM) and A-star (A*) algorithms. Created using a layered vector field strategy, this allows for flexibility along with the ability to add and remove layers to take into account other parameters such as currents, wind, dynamics, and the International Regulations for Preventing Collisions at Sea (COLGREGS). Different weights can be attributed to each layer based on the determined level of importance in a hierarchical manner. Different obstacle scenarios are shown in simulation, and proof-of-concept validation of the path-planning algorithms on an actual ASV is accomplished in an indoor environment. Results show that the combination of PFM and A* complement each other to generate a successfully planned path to goal that alleviates local minima and entrapment issues. Additionally, the planner demonstrates the ability to update for new obstacles in real time using an obstacle detection sensor. Regarding test and evaluation of autonomous vehicles, trust and confidence in autonomous behavior is required to send autonomous vehicles into operational missions. The author introduces the Performance Evaluation and Review Framework Of Robotic Missions (PERFORM), a framework for which to enable a rigorous and replicable autonomy test environment, thereby filling the void between that of merely simulating autonomy and that of completing true field missions. A generic architecture for defining the missions under test is proposed and a unique Interval Type-2 Fuzzy Logic approach is used as the foundation for the mathematically rigorous autonomy evaluation framework. The test environment is designed to aid in (1) new technology development (i.e. providing direct comparisons and quantitative evaluations of varying autonomy algorithms), (2) the validation of the performance of specific autonomous platforms, and (3) the selection of the appropriate robotic platform(s) for a given mission type (e.g. for surveying, surveillance, search and rescue). Several case studies are presented to apply the metric to various test scenarios. Results demonstrate the flexibility of the technique with the ability to tailor tests to the user’s design requirements accounting for different priorities related to acceptable risks and goals of a given mission

PRE working paper series : numbers 667 to 722

This paper contains a numerical listing of working papers produced by the Policy, Planning, and External Affairs Complex. Each citation contains a brief abstract, and the contact point for the paper.Environmental Economics&Policies,Economic Theory&Research,Public Sector Economics&Finance,Economic Stabilization,Banks&Banking Reform