Improving performance through concept formation and conceptual clustering

Research from June 1989 through October 1992 focussed on concept formation, clustering, and supervised learning for purposes of improving the efficiency of problem-solving, planning, and diagnosis. These projects resulted in two dissertations on clustering, explanation-based learning, and means-ends planning, and publications in conferences and workshops, several book chapters, and journals; a complete Bibliography of NASA Ames supported publications is included. The following topics are studied: clustering of explanations and problem-solving experiences; clustering and means-end planning; and diagnosis of space shuttle and space station operating modes

Building and Refining Abstract Planning Cases by Change of Representation Language

ion is one of the most promising approaches to improve the performance of problem solvers. In several domains abstraction by dropping sentences of a domain description -- as used in most hierarchical planners -- has proven useful. In this paper we present examples which illustrate significant drawbacks of abstraction by dropping sentences. To overcome these drawbacks, we propose a more general view of abstraction involving the change of representation language. We have developed a new abstraction methodology and a related sound and complete learning algorithm that allows the complete change of representation language of planning cases from concrete to abstract. However, to achieve a powerful change of the representation language, the abstract language itself as well as rules which describe admissible ways of abstracting states must be provided in the domain model. This new abstraction approach is the core of Paris (Plan Abstraction and Refinement in an Integrated System), a system in which abstract planning cases are automatically learned from given concrete cases. An empirical study in the domain of process planning in mechanical engineering shows significant advantages of the proposed reasoning from abstract cases over classical hierarchical planning.Comment: See http://www.jair.org/ for an online appendix and other files accompanying this articl

A Proposed Perspective Shift: Viewing Specification Design as a Planning Problem

16 pagesWe argue that in certain problem domains, AI planning can be viewed as a foundation for generation, critiquing, and elaboration of a specification. Two specification design projects in our group are used as a focus of discussion

Modelo de planificación y ejecución concurrente para la composición de servicios web semánticos en entornos parcialmente observables

Los servicios Web (SW) son componentes de software que pueden ser expuestos sobre Internet e invocados a través de protocolos estándar. Incorporar semánticas a los SW, tiene como objetivo describir los aspectos semánticos, además de los sintácticos de los propios servicios. Tales descripciones permiten a los componentes de software interactuar automáticamente a fin de lograr determinadas tareas sobre los servicios, entre las que se destaca la composición de servicios en servicios más complejos. Grandes esfuerzos se realizan en este campo de la composición, pero a pesar de lo exitosas que puedan ser las aproximaciones planteadas a la fecha, aún se caracterizan por no enfrentar conjuntamente algunos factores inherentes a la Web: el ser un entorno parcialmente observable, el comportamiento incierto de los servicios y las restricciones de tiempo en las respuestas de composición. Un promisorio enfoque orientado a este fin, es el liderado por la comunidad de la Inteligencia Artificial (IA), la cual enfrenta la composición de servicios Web mediante la aplicación de técnicas de planificación IA. Es así como en este trabajo de tesis de doctorado, se propone un modelo que permita llevar a cabo la composición automática de Servicios Web Semánticos (SWS), integrando concurrentemente procesos de planificación y ejecución con restricciones de tiempo. De esta forma, se adquiere progresivamente solo la información esencialmente requerida del estado actual de la Web limitando la respuesta (un plan de composición), a un período de tiempo especificado, superando conjuntamente, dificultades propias del dominio del problema como las antes mencionadas. / Abstract. Web Services (WS) can be defined as software components that can be exposed and called on the Internet using standard communication protocols. To include semantic mechanisms in WS is aim at describing semantics aspects of the services as well as syntactic ones. Such kinds of descriptions allow software components automatically interact in order to achieve certain tasks applied on services, among which, it should be highlighted the composition of services to obtain more complex services. Great efforts have already being made within the WS composition field, but in spite of those successful approaches that can be raised to date; they are still characterized for being unaware of some key issues inherent to the Web: the fact to be a partially observable environment, the services’ uncertain behavior, and time constraints related to WS composition responses. A promising approach aimed at this purpose is headed by the Artificial Intelligence (AI) community that faces the WS composition based on the application of AI planning techniques. Thus, in this doctoral thesis dissertation, a model to perform the automatic composition of Semantic Web Services (SWS) is proposed which concurrently integrates AI planning and execution processes under time constraints. In this way, this model gradually acquires only the required essential information of the Web’s current state, and restricts the response (a composition plan) to a given time period, overcoming together, those difficulties inherent to the problem domain as mentioned above.Maestrí