The Architect's Collaborator: Toward Intelligent Tools for Conceptual Design

In early stages of architectural design, as in other design domains, the language used is often very abstract. In architectural design, for example, architects and their clients use experiential terms such as "private" or "open" to describe spaces. If we are to build programs that can help designers during this early-stage design, we must give those programs the capability to deal with concepts on the level of such abstractions. The work reported in this thesis sought to do that, focusing on two key questions: How are abstract terms such as "private" and "open" translated into physical form? How might one build a tool to assist designers with this process? The Architect's Collaborator (TAC) was built to explore these issues. It is a design assistant that supports iterative design refinement, and that represents and reasons about how experiential qualities are manifested in physical form. Given a starting design and a set of design goals, TAC explores the space of possible designs in search of solutions that satisfy the goals. It employs a strategy we've called dependency-directed redesign: it evaluates a design with respect to a set of goals, then uses an explanation of the evaluation to guide proposal and refinement of repair suggestions; it then carries out the repair suggestions to create new designs. A series of experiments was run to study TAC's behavior. Issues of control structure, goal set size, goal order, and modification operator capabilities were explored. In addition, TAC's use as a design assistant was studied in an experiment using a house in the process of being redesigned. TAC's use as an analysis tool was studied in an experiment using Frank Lloyd Wright's Prairie houses.

Reinforcement Learning by Policy Search

One objective of artificial intelligence is to model the behavior of an intelligent agent interacting with its environment. The environment's transformations can be modeled as a Markov chain, whose state is partially observable to the agent and affected by its actions; such processes are known as partially observable Markov decision processes (POMDPs). While the environment's dynamics are assumed to obey certain rules, the agent does not know them and must learn. In this dissertation we focus on the agent's adaptation as captured by the reinforcement learning framework. This means learning a policy---a mapping of observations into actions---based on feedback from the environment. The learning can be viewed as browsing a set of policies while evaluating them by trial through interaction with the environment. The set of policies is constrained by the architecture of the agent's controller. POMDPs require a controller to have a memory. We investigate controllers with memory, including controllers with external memory, finite state controllers and distributed controllers for multi-agent systems. For these various controllers we work out the details of the algorithms which learn by ascending the gradient of expected cumulative reinforcement. Building on statistical learning theory and experiment design theory, a policy evaluation algorithm is developed for the case of experience re-use. We address the question of sufficient experience for uniform convergence of policy evaluation and obtain sample complexity bounds for various estimators. Finally, we demonstrate the performance of the proposed algorithms on several domains, the most complex of which is simulated adaptive packet routing in a telecommunication network.

Electrical Design: A Problem for Artificial Intelligence Research

This report outlines the problem of intelligent failure recovery in a problem-solver for electrical design. We want our problem solver to learn as much as it can from its mistakes. Thus we cast the engineering design process on terms of Problem Solving by Debugging Almost-Right Plans, a paradigm for automatic problem solving based on the belief that creation and removal of "bugs" is an unavoidable part of the process of solving a complex problem. The process of localization and removal of bugs called for by the PSBDARP theory requires an approach to engineering analysis in which every result has a justification which describes the exact set of assumptions it depends upon. We have developed a program based on Analysis by Propagation of Constraints which can explain the basis of its deductions. In addition to being useful to a PSBDARP designer, these justifications are used in Dependency-Directed Backtracking to limit the combinatorial search in the analysis routines. Although the research we will describe is explicitly about electrical circuits, we believe that similar principles and methods are employed by other kinds of engineers, including computer programmers.

Exploring Bit-Difference for Approximate KNN Search in High-dimensional Databases

In this paper, we develop a novel index structure to support efficient approximate k-nearest neighbor (KNN) query in high-dimensional databases. In high-dimensional spaces, the computational cost of the distance (e.g., Euclidean distance) between two points contributes a dominant portion of the overall query response time for memory processing. To reduce the distance computation, we first propose a structure (BID) using BIt-Difference to answer approximate KNN query. The BID employs one bit to represent each feature vector of point and the number of bit-difference is used to prune the further points. To facilitate real dataset which is typically skewed, we enhance the BID mechanism with clustering, cluster adapted bitcoder and dimensional weight, named the BID⁺. Extensive experiments are conducted to show that our proposed method yields significant performance advantages over the existing index structures on both real life and synthetic high-dimensional datasets.

Keyword Join: Realizing Keyword Search in P2P-based Database Systems

In this paper, we present a P2P-based database sharing system that provides information sharing capabilities through keyword-based search techniques. Our system requires neither a global schema nor schema mappings between different databases, and our keyword-based search algorithms are robust in the presence of frequent changes in the content and membership of peers. To facilitate data integration, we introduce keyword join operator to combine partial answers containing different keywords into complete answers. We also present an efficient algorithm that optimize the keyword join operations for partial answer integration. Our experimental study on both real and synthetic datasets demonstrates the effectiveness of our algorithms, and the efficiency of the proposed query processing strategies.

Autonomous underwater vehicle control using reinforcement learning policy search methods

Autonomous underwater vehicles (AUV) represent a challenging control problem with complex, noisy, dynamics. Nowadays, not only the continuous scientific advances in underwater robotics but the increasing number of subsea missions and its complexity ask for an automatization of submarine processes. This paper proposes a high-level control system for solving the action selection problem of an autonomous robot. The system is characterized by the use of reinforcement learning direct policy search methods (RLDPS) for learning the internal state/action mapping of some behaviors. We demonstrate its feasibility with simulated experiments using the model of our underwater robot URIS in a target following task

Towards Direct Policy Search Reinforcement Learning for Robot Control

This paper proposes a high-level reinforcement learning (RL) control system for solving the action selection problem of an autonomous robot. Although the dominant approach, when using RL, has been to apply value function based algorithms, the system here detailed is characterized by the use of direct policy search methods. Rather than approximating a value function, these methodologies approximate a policy using an independent function approximator with its own parameters, trying to maximize the future expected reward. The policy based algorithm presented in this paper is used for learning the internal state/action mapping of a behavior. In this preliminary work, we demonstrate its feasibility with simulated experiments using the underwater robot GARBI in a target reaching task

Effects of memory load on visual search

Resumen tomado de la publicaci??n

Manejo de cardiomiopatia chagasica : revisión sistemática (1996-2010)

La cardiomiopatía chagásica es la más importante y severa manifestación de la enfermedad crónica, los pacientes pueden cursar con falla cardiaca, arritmias, bloqueos cardiacos, tromboembolismo y muerte súbita. El diagnóstico es tardío, debido a que se confunden con cardiopatías de otra etiología y el manejo se realiza con base en guías y protocolos dirigidos hacia el tratamiento de falla cardiaca de origen no chagásico. Métodos: Se realizó una revisión sistemática y tuvo como objetivo responder a las siguientes Preguntas clínicas: PREGUNTA 1. ¿El manejo actual para la cardiomiopatía chagásica (betabloqueadores, IECA, ARA II, Diuréticos, Inhibidores de la fosfodiesterasa, Estatinas, antiagragantes plaquetarios) que es extrapolado del manejo de falla cardiaca de origen no chagásico tiene impacto en la calidad de vida, sobrevida, seguridad, estancia hospitalaria y disminución del número de hospitalizaciones, mejoría de síntomas, de los pacientes adultos con cardiopatía chagásica?. PREGUNTA 2. ¿En pacientes con cardiomiopatía chagásica el uso de fármacos tripanocidas mejora la sobrevida, calidad de vida, estancia hospitalaria, disminución del número de hospitalizaciones, y resolución de síntomas? PREGUNTA 3. ¿En pacientes con cardiomiopatía chagásica el uso de cardiodesfibriladores mejora la sobrevida, calidad de vida, estancia hospitalaria, disminución del número de hospitalizaciones, y resolución de síntomas? PREGUNTA 4. ¿En pacientes con cardiomiopatía chagásica el uso de marcapasos mejora la sobrevida, calidad de vida, estancia hospitalaria, disminución del número de hospitalizaciones, y resolución de síntomas? PREGUNTA 5. ¿En pacientes con cardiomiopatía chagásica el uso de trasplante de corazón mejora la sobrevida, calidad de vida, estancia hospitalaria, disminución del número de hospitalizaciones, y resolución de síntomas? Se realizaron búsquedas en: MEDLINE, Colaboración Cochrane, Trip database, y otras importantes bases de datos desde 1996 hasta 2010, limitando la búsqueda. Los estudios se seleccionaron de acuerdo a criterios de pertinencia PICO y se evaluó la calidad, usando la metodología recomendada en Scottish Intercollegiate Guidelines Network. Resultados: Se encontraron 21 estudios, que incluyen revisiones sistemáticas, ensayos clínicos controlados y aleatorizados, ensayos clínicos, cohortes y, casos y controles. Estos estudios cumplieron con los criterios de inclusión. Discusión: En esta revisión sistemática se presenta un consolidado de la evidencia disponible acerca de la eficacia de las siguientes intervenciones: Betabloqueadores, IECAS, PDE, Digoxina, nitroderivados, cardiodesfibriladores, marcapasos y trasplante de corazón, en pacientes con cardiopatía chagásica; los estudios encontrados en su mayoría son de baja evidencia.

TU Graz: Course: 707.000 Web Science and Web Technology: Lecture 7: Link Analysis and Search

What are ways of searching in graphs? In this class, we will discuss basics of link analysis, including Google's PageRank algorithm as an example. Readings: The PageRank Citation Ranking: Bringing Order to the Web, L. Page and S. Brin and R. Motwani and T. Winograd (1998) Stanford Tecnical Report

SPSS Topics Search - An animated guide

Need help on a given topic? Topics Search tab can help narrow down the search.

Library Guide: improving your search & Search Planner

A search planner to help structure a search (for journal articles in bibliographic databases). Also includes hints and tips on improving your search. This is a generic guide, useful for any subject.

Análisis de la política exterior de los Estados Unidos hacia Pakistán en el período 2001-2010

A partir del año 2001 Pakistán se convirtió en un aliado estratégico para los intereses de Washington en la región. Debido a los ataques del 11-S perpetrados por Al-Qaeda, el panorama de seguridad mundial cambia y Washington decide intervenir con mayor liderazgo en esta materia. Así pues, la política exterior dirigida hacia Pakistán en el período 2001-2010, es un ejemplo claro de cómo EE.UU. por un lado, redefine diferentes conceptos de seguridad en aras de justificar sus actuaciones y por otro, emprende acciones en política exterior que le permiten además de neutralizar las nuevas amenazas, ir en búsqueda de sus intereses en las regiones identificadas como prioritarias para la consecución de sus objetivos nacionales. Es así como, la alianza con Pakistán es una de las estrategias que encuentra EE.UU. para perseguir sus pretensiones políticas, geoestratégicas y de seguridad en el Gran Medio Oriente.