Intelligence by Design: Principles of Modularity and Coordination for Engineerin

All intelligence relies on search --- for example, the search for an intelligent agent's next action. Search is only likely to succeed in resource-bounded agents if they have already been biased towards finding the right answer. In artificial agents, the primary source of bias is engineering. This dissertation describes an approach, Behavior-Oriented Design (BOD) for engineering complex agents. A complex agent is one that must arbitrate between potentially conflicting goals or behaviors. Behavior-oriented design builds on work in behavior-based and hybrid architectures for agents, and the object oriented approach to software engineering. The primary contributions of this dissertation are: 1.The BOD architecture: a modular architecture with each module providing specialized representations to facilitate learning. This includes one pre-specified module and representation for action selection or behavior arbitration. The specialized representation underlying BOD action selection is Parallel-rooted, Ordered, Slip-stack Hierarchical (POSH) reactive plans. 2.The BOD development process: an iterative process that alternately scales the agent's capabilities then optimizes the agent for simplicity, exploiting tradeoffs between the component representations. This ongoing process for controlling complexity not only provides bias for the behaving agent, but also facilitates its maintenance and extendibility. The secondary contributions of this dissertation include two implementations of POSH action selection, a procedure for identifying useful idioms in agent architectures and using them to distribute knowledge across agent paradigms, several examples of applying BOD idioms to established architectures, an analysis and comparison of the attributes and design trends of a large number of agent architectures, a comparison of biological (particularly mammalian) intelligence to artificial agent architectures, a novel model of primate transitive inference, and many other examples of BOD agents and BOD development.

Learning from Ambiguity

There are many learning problems for which the examples given by the teacher are ambiguously labeled. In this thesis, we will examine one framework of learning from ambiguous examples known as Multiple-Instance learning. Each example is a bag, consisting of any number of instances. A bag is labeled negative if all instances in it are negative. A bag is labeled positive if at least one instance in it is positive. Because the instances themselves are not labeled, each positive bag is an ambiguous example. We would like to learn a concept which will correctly classify unseen bags. We have developed a measure called Diverse Density and algorithms for learning from multiple-instance examples. We have applied these techniques to problems in drug design, stock prediction, and image database retrieval. These serve as examples of how to translate the ambiguity in the application domain into bags, as well as successful examples of applying Diverse Density techniques.

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.

Sequential Optimal Recovery: A Paradigm for Active Learning

In most classical frameworks for learning from examples, it is assumed that examples are randomly drawn and presented to the learner. In this paper, we consider the possibility of a more active learner who is allowed to choose his/her own examples. Our investigations are carried out in a function approximation setting. In particular, using arguments from optimal recovery (Micchelli and Rivlin, 1976), we develop an adaptive sampling strategy (equivalent to adaptive approximation) for arbitrary approximation schemes. We provide a general formulation of the problem and show how it can be regarded as sequential optimal recovery. We demonstrate the application of this general formulation to two special cases of functions on the real line 1) monotonically increasing functions and 2) functions with bounded derivative. An extensive investigation of the sample complexity of approximating these functions is conducted yielding both theoretical and empirical results on test functions. Our theoretical results (stated insPAC-style), along with the simulations demonstrate the superiority of our active scheme over both passive learning as well as classical optimal recovery. The analysis of active function approximation is conducted in a worst-case setting, in contrast with other Bayesian paradigms obtained from optimal design (Mackay, 1992).

A Formulation for Active Learning with Applications to Object Detection

We discuss a formulation for active example selection for function learning problems. This formulation is obtained by adapting Fedorov's optimal experiment design to the learning problem. We specifically show how to analytically derive example selection algorithms for certain well defined function classes. We then explore the behavior and sample complexity of such active learning algorithms. Finally, we view object detection as a special case of function learning and show how our formulation reduces to a useful heuristic to choose examples to reduce the generalization error.

Space System Architecture: Final Report of SSPARC: the Space Systems, Policy, and Architecture Research Consortium (Thrust I and II)

The Space Systems, Policy and Architecture Research Consortium (SSPARC) was formed to make substantial progress on problems of national importance. The goals of SSPARC were to: • Provide technologies and methods that will allow the creation of flexible, upgradable space systems, • Create a “clean sheet” approach to space systems architecture determination and design, including the incorporation of risk, uncertainty, and flexibility issues, and • Consider the impact of national space policy on the above. This report covers the last two goals, and demonstrates that the effort was largely successful.

Designing context-aware adaptive units of learning based on IMS-LD standard

Learning contents adaptation has been a subject of interest in the research area of the adaptive hypermedia systems. Defining which variables and which standards can be considered to model adaptive content delivery processes is one of the main challenges in pedagogical design over e-learning environments. In this paper some specifications, architectures and technologies that can be used in contents adaptation processes considering characteristics of the context are described and a proposal to integrate some of these characteristics in the design of units of learning using adaptation conditions in a structure of IMS-Learning Design (IMS-LD) is presented. The key contribution of this work is the generation of instructional designs considering the context, which can be used in Learning Management Systems (LMSs) and diverse mobile devices

Adaptive intelligent agent approach to guide the web navigation on the PLAN-G distance learning platform

Hypermedia systems based on the Web for open distance education are becoming increasingly popular as tools for user-driven access learning information. Adaptive hypermedia is a new direction in research within the area of user-adaptive systems, to increase its functionality by making it personalized [Eklu 961. This paper sketches a general agents architecture to include navigational adaptability and user-friendly processes which would guide and accompany the student during hislher learning on the PLAN-G hypermedia system (New Generation Telematics Platform to Support Open and Distance Learning), with the aid of computer networks and specifically WWW technology [Marz 98-1] [Marz 98-2]. The PLAN-G actual prototype is successfully used with some informatics courses (the current version has no agents yet). The propased multi-agent system, contains two different types of adaptive autonomous software agents: Personal Digital Agents {Interface), to interacl directly with the student when necessary; and Information Agents (Intermediaries), to filtrate and discover information to learn and to adapt navigation space to a specific student

Aula de música : desarrollo de contenidos en una herramienta de e-learning para la asignatura de música en Educación Primaria.

Aula de música es una herramienta e-learning para el desarrollo del aprendizaje de la música para niños con edades comprendidas entre los 6 y 12 años, edades correspondientes a las de los alumnos de la etapa de la Educación Primaria. En esta herramienta destaca el uso de estándares y especificaciones como LOM, IMS, etc. que van a facilitar la tarea de reutilizar la documentación incluida para compartir conocimiento. El proceso de elaboración del contenido ha sido fundamental y en relación con el entorno de trabajo debe mencionarse que se ha primado la construcción de una GUI que sirva para aprender y que motive a los alumnos a aprender música de una forma diferente, en contraposición a realizar una diseño estético que fuera incapaz de adaptarse a las capacidades de cada tipo de usuario, para lo que se han tenido en cuenta criterios de usabilidad y accesibilidad (WAI).

VADS: free art and design images for education

VADS is the online resource for visual arts. It has provided services to the academic community for 12 years and has built up a considerable portfolio of visual art collections comprising over 100,000 images that are freely available and copyright cleared for use in learning, teaching and research in the UK.

E Assessment for Web Design Labs

Small quizzes designed to reinforce learning from web design labs.

Technology Enhanced Learning at Southampton

A short overview of TEL intended for a short PCAP workshop

Intervention strategies on teachers focused on creating meaningful educational environments for mathematical abilities development

This article describes an intervention process undertaken in a training program for preschool and first grade teachers from public schools in Cali, Colombia. The objective of this process is to provide a space for teachers to reflect on pedagogical practices which allow them to generate educational processes that foster children’s understanding of mathematical knowledge in the classroom. A set of support strategies was presented for helping teachers in the design, analysis and implementation of learning environments as meaningful educational spaces. Furthermore, participants engaged in an analysis of their own intervention modalities to identify which modalities facilitate the development of mathematical abilities in children. In order to ascertain the transformations in the teachers’ learning environments, the mathematical competences and cognitive processes underlying the activities proposed in the classroom, as well as teacher intervention modalities and the types of student participation in classroom activities were examined both before and after the intervention process. Transformations in the teachers’ conceptions about the children’s abilities and their own practices in teaching mathematics in the classroom were evidenced.

ARTINTEC S.A.S.: propuestas interactivas con contenido educativo

ArtInTec es una organización creativa que ofrece servicios complementarios de apoyo pedagógico mediante instalaciones artísticas que integran arte y tecnología, unión que permite abarcar tres conceptos fundamentales: interactividad, lograda a través de herramientas tecnológicas convencionales y no convencionales; experiencias cognitivas significativas, mediante la generación de procesos de aprendizaje innovadores que permitan la construcción de saberes de trascendencia, y desarrollo de contenidos de diferentes temáticas que fortalezcan el aprendizaje. En su primera etapa, ArtInTec estará enfocada en el diseño, producción, comercialización, divulgación y venta de servicios transversales de apoyo pedagógico que impliquen la intermediación del arte y la tecnología, partiendo del concepto de instalación artística como género del arte contemporáneo que utiliza directamente el espacio de exposición como escenario para ser transitado por el espectador, logrando así una experiencia interactiva. Los tres servicios comparten características que se constituyen como columna vertebral de su naturaleza: tienen un objetivo altamente pedagógico, las temáticas son desarrolladas previo consenso entre la institución y ArtInTec, su componente artístico es el hilo conductor de la experiencia y se desarrollan en las instalaciones de la institución contratante. A continuación se enuncia cada uno de los servicios ofrecidos: a. Taller asistido por nuevas tecnologías. b. Instalación interactiva en gran formato. c. Instalación interactiva para dispositivos móviles.

Teaching primary art and design.

Esta guía ayuda a los profesores en período de formación inicial a superar el Qualified Teacher Status (QTS), aunque también abarca áreas clave del currículo de arte y diseño para las etapas de infantil y primaria. Para ello, utiliza estudios de casos como ejemplos para el desarrollo práctico de los marcos curriculares en el aula. Asimismo, ofrece consejos prácticos sobre identificación de temas, preparación de recursos en aprendizaje, planificación y evaluación.