Time-constrained reasoning under uncertainty

Bonissone, Piero P.; Halverson, Peter C.

doi:10.1007/bf01840465

Cited by 29 publications

(4 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…At the representation level, intelligent systems are verified through assessments of the adequacy of systems to efficiently represent and propagate information through a knowledge base, and evaluations of a system's ability to revise its conclusions as new data becomes available (Bonissone & Halverson, 1990). Performance metrics for these assessments typically include numbers and types of representational errors committed by the system (Stefik, Aikens, Balzer, Benoit, Birnbaum, Hayes-Roth, & Sacerdoti, 1983).…”

Section: Eirts Evaluation Criteriamentioning

confidence: 99%

Evaluation of Embedded Intelligent Real‐time Systems*

Grabowski

Sanborn

2001

Decision Sciences

View full text Add to dashboard Cite

Over the past two decades, questions have surfaced about the effectiveness and contribution of intelligent systems to decision makers in a variety of settings. This paper focuses on the evaluation challenges associated with intelligent real-time software systems that are embedded in larger host systems. With the proliferation of such systems in operational settings such as aerospace, medical, manufacturing, and transportation systems, increased attention to evaluations of such systems, and to resulting software safety, is warranted. This paper describes one such evaluation and proposes a set of evaluation criteria for embedded intelligent real-time systems (EIRTS). Implications of the evaluation and the evaluation criteria are discussed.the masters and crew of the Exxon Benicia and other trans-Alaskan Pipeline System (TAPS) trade tankers who participated in this research. The authors would also like to thank A1 Wallace, two anonymous reviewers, and the associate editor, who contributed greatly to this manuscript. 95Evaluation of Embedded Intelligent Real-Time Systems support systems, knowledge-based systems, and expert systems. These systems were initially conceived as stand-alone information technology, designed to provide decision support to their users in a timely and appropriate fashion. Recently, such systems have evolved from stand-alone systems and integrated systems, which share resources and information with other systems, to embedded intelligent real-time systems (EIRTS), which are knowledge-based systems deployed in larger host systems with real-time response requirements. These systems are essential to the functioning of many safety-critical large-scale systems, such as air traffic control systems, worldwide financial and telecommunications systems, and real-time command and control networks.The urge to develop embedded intelligent real-time systems to guide users' actions in complex large scale systems has been described as "almost irresistible," despite questions about the effectiveness or contribution of such systems to decision makers: Given decades of research on expert systems, advisory systems, and operator aids, there are few operational success stories. Empirical studies show that advice-giving systems frequently fail to enhance overall system performance. Sometimes operators fail to request the proffered advice. Sometimes operators do not like the "tone" of the advice-giving agent and thus refuse to follow its advice. Sometimes the operators do not feel the advice is worth seeking. Other aids are effective for novices, but as operators acquire skill, the aid no longer has measurable effect (Mitchell & Sundstrom, 1997, p. 269).These observations suggest that assessments of EIRTS are important, particularly in safety-critical large-scale systems such as aviation and aerospace where software safety (Leveson, 1995) is important. In these systems, humans and technology are often jointly responsible for executing tasks and, thus, both technical and organizational evaluation measures are important....

show abstract

Section: Eirts Evaluation Criteriamentioning

confidence: 99%

Evaluation of Embedded Intelligent Real‐time Systems*

Grabowski

Sanborn

2001

Decision Sciences

View full text Add to dashboard Cite

show abstract

“…Associated with each task is a set of methods with their associated processing times and solution qualities. Each method consists of a set of subtasks, each of which can have a distinct earliest start time (for example, because of data arrival times or dependencies on earlier subtasks), and each of which has an estimated processing time 3 .…”

Section: A Design-to-time Scheduling Algorithmmentioning

confidence: 99%

“…It appears that Bonissone and Halverson[3] were the first to use the term "design-to-time" to refer to systems of the form described by D'Ambrosio.…”

mentioning

confidence: 99%

Design-to-time real-time scheduling

Garvey

Lesser

1993

IEEE Trans. Syst., Man, Cybern.

126

View full text Add to dashboard Cite

Design-to-time is an approach to problem-solving in resource-constrained domains where: multiple solution methods are available for tasks, those solution methods make tradeoffs in solution quality versus time, and satisficing solutions are acceptable. Design-to-time involves designing a solution to a problem that uses all available resources to maximize the solution quality within the available time. This paper defines the design-to-time approach in detail, contrasting it to the anytime algorithm approach, and presents a heuristic algorithm for designto-time real-time scheduling. Our blackboard architecture that implements the design-to-time approach is discussed and an example problem and solution from the Distributed Vehicle Monitoring Testbed (DVMT) is described in detail. Experimental results, generated using a simulation, show the effects of various parameters on scheduler performance. Finally we discuss future research goals and plans.

show abstract

“…Bonissone y Halverson en [Bon90] presentan una técnica basada en disponer de planes precompilados de los cuales se hace una predicción de su utilidad. En tiempo de ejecución se selecciona uno de los planes, en función de las circunstancias del problema y del tiempo disponible uno de los planes, teniendo en cuenta además ejecuciones previas de los planes para poder estimar con mayor precisión los resultados que se pueden obtener.…”

Section: Técnicas De Resolución De Problemas En Tiempo Realunclassified

Heurísticas para el control deliberativo en una arquitectura de agentes inteligentes de tiempo real

López¹

View full text Add to dashboard Cite

Valencia, abril de 2004.A mis padres y hermanos."Efectuar elecciones viables se produce en un crisol de errores informativos. Así acepta la Inteligencia la fiabilidad. Y cuando no son conocidas las elecciones absolutas (infalibles), la Inteligencia corre sus riesgos con los datos limitados que posee en una arena donde los errores no sólo son posibles sino necesarios.-Darwi Odrade". Casa capitular: Dune. Frank Herbert."La calidad nunca es un accidente; siempre es el resultado de un esfuerzo de la inteligencia." John Ruskin I ReconocimientosQuiero agradecer este trabajo en primer lugar a mis directores, Vicente Botti y Ana García Fornes, por su dedicación y ayuda para poder realizar este trabajo.También mi agradecimiento a todos los miembros del grupo al que pertenezco, el grupo de Tecnología Informática e Inteligencia Artificial (GTI-IA). Especialmente quiero destacar a Andrés Terrasa e Ignacio Pachés por su trabajo en la arquitectura ARTIS sin el cual no podría haberse llevado a cabo este trabajo. También a Carlos Carrascosa por su trabajo en el módulo de control de la arquitectura ARTIS. II III ResumenEl área de la Inteligencia Artificial está experimentando un gran avance en los últimos tiempos con su aplicación a un mayor número de campos diferentes. Uno de ellos es el de los problemas de tiempo real. Problemas donde no sólo es importante la lógica del cálculo de las soluciones, sino también el instante de tiempo en que son calculadas dichas soluciones.Este acercamiento entre ambas áreas es, en principio, provechoso, pues la Inteligencia Artificial puede aportar nuevas posibilidades a los sistemas de tiempo real, como una mayor flexibilidad de adaptación a entornos complejos y dinámicos. Sin embargo esta aproximación ha presentado desde siempre importantes dificultades.Principalmente los sistemas de tiempo real poseen unos requerimientos temporales (predecibilidad de los tiempos de respuesta principalmente) que no suelen ser habituales en las técnicas de Inteligencia Artificial.Entre otras formas de abordar este problema, está el desarrollo de arquitecturas software para el diseño de agentes inteligentes para su uso en entornos de tiempo real. Estas arquitecturas poseen diferentes mecanismos para que los agentes construidos puedan trabajar en entornos de tiempo real ofreciendo comportamientos reactivos (para cumplir los requerimientos temporales) y deliberativos (que hacen uso de técnicas de Inteligencia Artificial para conseguir mejores prestaciones).Una de estas arquitecturas es ARTIS. Esta arquitectura hace uso de una planificación de sus tareas a dos niveles para conseguir cumplir sus objetivos. Por un lado un planificador de primer nivel garantiza la obtención de respuestas dentro de límites temporales estrictos. Un planificador de segundo nivel se encarga del control de componentes que mejoran la calidad de los resultados.El trabajo presentado se centra en este segundo planificador, desarrollando dos heurísticas, SSS (slack-slide scheduling, scheduling por desplazamiento del slack) y SSSM (SSS con Memor...

show abstract

Time-constrained reasoning under uncertainty

Cited by 29 publications

References 13 publications

Evaluation of Embedded Intelligent Real‐time Systems*

Evaluation of Embedded Intelligent Real‐time Systems*

Design-to-time real-time scheduling

Heurísticas para el control deliberativo en una arquitectura de agentes inteligentes de tiempo real

Contact Info

Product

Resources

About