The entropy reduction engine: Integrating planning, scheduling, and control

The Entropy Reduction Engine, an architecture for the integration of planning, scheduling, and control, is described. The architecture is motivated, presented, and analyzed in terms of its different components; namely, problem reduction, temporal projection, and situated control rule execution. Experience with this architecture has motivated the recent integration of learning. The learning methods are described along with their impact on architecture performance

Anytime synthetic projection: Maximizing the probability of goal satisfaction

A projection algorithm is presented for incremental control rule synthesis. The algorithm synthesizes an initial set of goal achieving control rules using a combination of situation probability and estimated remaining work as a search heuristic. This set of control rules has a certain probability of satisfying the given goal. The probability is incrementally increased by synthesizing additional control rules to handle 'error' situations the execution system is likely to encounter when following the initial control rules. By using situation probabilities, the algorithm achieves a computationally effective balance between the limited robustness of triangle tables and the absolute robustness of universal plans

Characterization of Functionality in a Dynamic Environment

Identifying the functionality in objects means to be able to associate a purpose with them in a specific environment. The purpose depends on the intention of the agent and on the applicability of the object in a particular task. In our investigation of functionality we focus on functionalities which involve changes of physical relation and properties between objects in the environment. A formal model, based on Discrete Event Dynamic System Theory (DEDS), is introduced to define an interactive task for recovering and describing functionality. To observe and control the recovery process we introduce the notion of piecewise observability of a task by different sensors. This allows the description of a dynamic system in which neither all events nor the time of their occurrence may be predicted in advance. We have developed an experimental system consisting of actuators and both force and position sensors, for carrying out the interactive recovery of functionality. In particular, we demonstrate how this approach can be used by carrying out some experiments investigating the functionality of piercing. Furthermore, we discuss the importance of a multisensory approach for the observation and interpretation of functionality

Modelo de detección y seguimiento de anomalías en entornos monitoreados por agentes robóticos inteligentes

En diversas partes del planeta existen entornos que requieren ser monitoreados en tiempo real para evitar catástrofes tales como incendios, derrames de petróleo o fugas radioactivas. En la presente proyecto de tesis se propone un modelo basado en agentes robóticos inteligentes para el proceso de detección y seguimiento de anomalías generadas por cambios drásticos en variables físicas. El modelo propone un enfoque basado en inteligencia artificial bayesiana para determinar el comportamiento de los gentes robóticos a partir de la evidencia tomada por los sensores de variables físicas del entorno. Adicionalmente, se integra una nueva propuesta en el proceso de seguimiento basado en la técnica de control Proporcional Integral Derivativo (PID). En la parte preliminar se realiza una revisión de literatura sobre las técnicas de detección y seguimiento de perímetros de anomalías con sensores robóticos; posteriormente se describe el modelo propuesto y se valida sobre la plataforma de desarrollo y simulación de sensores móviles MobSim. Finalmente se evalúa el desempeño y se compara la propuesta con otras técnicas relacionadas para llegar a un análisis de resultados y conclusiones./ Abstract: Around the world there are environments that need to be monitored in real time to prevent disasters such as fires, oil spills and radiation leaks. In this thesis project we propose a model based on intelligent robotic agents for detection and tracking anomalies generated by drastic changes in physical variables. The model proposes an approach based on Bayesian artificial intelligence to determine the behavior of robotic people from the evidence taken by the sensors of physical variables in the environment. Additionally, it integrates a new proposal in the monitoring process technique based on proportional integral derivative (PID). The introductory part is a review of techniques for detecting and monitoring perimeters with a robotic sensor network. Then, we desribe the proposed model and it is validated on the development platform and simulation of mobile sensors MobSim. Finally, performance is evaluated and compared with related techniques to get results and concludeMaestrí

An expert system for temporal planning with an application to runway configuration management

January 24, 1991Also issued as a Ph. D. thesis, Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 1991Includes bibliographical references (p. 100-105)This thesis describes an expert system to aid in the management of operations in complex qualitative domains characterized by multiple parallel activities with time-critical relationships. An extension to "standard" temporal logic required for reasoning about inferred allocation of resources and a detailed representation of temporally dependent facts, including persistence, is presented. The non-linear planning paradigm commonly used in planning programs is extended into the temporal domain to facilitate scheduling as well as ordering of plan steps. This enhancement requires new structures and analytical methods for the detection and resolution of serendipitous interactions and conflicts between proposed schedules. A computer implementation of these concepts is discussed in detail. The expert system is organized into three modules: the time map manager or temporal database manager which stores, organizes, and retrieves time dependent knowledge; the temporal system analyzer which uses this knowledge to forecast and analyze domain dynamics; and the planner/scheduler which formulates and schedules activities in order to satisfy goals generated by the temporal system analyzer. Finally, Tower Chief, an application of the system to scheduling runway configuration changes and maintenance at large airports, is described.Research supported by the National Aeronautics and Space Administratio

Relevanzbasierte Informationsbeschaffung für die informierte Entscheidungsfindung intelligenter Agenten

This dissertation introduces relevance-based information acquisition for intelligent software agents based on Howard s information value theory and decision networks. Active information acquisition is crucial in domains with partial observability in order to establish situation awareness of autonomous systems for deliberate decisions. The new semi-myopic approach addresses the complexity challenge of decision-theoretic relevance computation by reducing the set of variables to be evaluated in the first place. Links in a decision network encode stochastic dependencies of variables. Through utility dependency analysis using Pearl s d-separation criterion, the set of relevant variables can be efficiently reduced to a proven minimum without actually computing information value. In addition to an implementation with detailed runtime performance analysis, the applicability of the approach is shown in the domain of intelligent logistics control