Arquitectura cognitiva basada en el gradiente sensorial y su aplicación a la robótica móvil

El objetivo de esta tesis es el desarrollo de un sistema completo de navegación, aprendizaje y planificación para un robot móvil. Dentro de los innumerables problemas que este gran objetivo plantea, hemos dedicado especial atención al problema del conocimiento autónomo del mundo. Nuestra mayor preocupación ha sido la de establecer mecanismos que permitan, a partir de información sensorial cruda, el desarrollo incremental de un modelo topológico del entorno en el que se mueve el robot. Estos mecanismos se apoyan invariablemente en un nuevo concepto propuesto en esta tesis: el gradiente sensorial. El gradiente sensorial es un dispositivo matemático que funciona como un detector de sucesos interesantes para el sistema. Una vez detectado uno de estos sucesos, el robot puede identificar su situación en un mapa topológico y actuar en consecuencia. Hemos denominado a estas situaciones especiales lugares sensorialmente relevantes, ya que (a) captan la atención del sistema y (b) pueden ser identificadas utilizando la información sensorial. Para explotar convenientemente los modelos construidos, hemos desarrollado un algoritmo capaz de elaborar planes internalizados, estableciendo una red de sugerencias en los lugares sensorialmente relevantes, de modo que el robot encuentra en estos puntos una dirección recomendada de navegación. Finalmente, hemos implementado un sistema de navegación robusto con habilidades para interpretar y adecuar los planes internalizados a las circunstancias concretas del momento. Nuestro sistema de navegación está basado en la teoría de campos de potencial artificial, a la que hemos incorporado la posibilidad de añadir cargas ficticias como ayuda a la evitación de mínimos locales. Como aportación adicional de esta tesis al campo genérico de la ciencia cognitiva, todos estos elementos se integran en una arquitectura centrada en la memoria, lo que pretende resaltar la importancia de ésta en los procesos cognitivos de los seres vivos y aporta un giro conceptual al punto de vista tradicional, centrado en los procesos. The general objective of this thesis is the development of a global navigation system endowed with planning and learning features for a mobile robot. Within this general objective we have devoted a special effort to the autonomous learning problem. Our main concern has been to establish the necessary mechanisms for the incremental development of a topological model of the robot’s environment using the sensory information. These mechanisms are based on a new concept proposed in the thesis: the sensory gradient. The sensory gradient is a mathematical device which works like a detector of “interesting” environment’s events. Once a particular event has been detected the robot can identify its situation in the topological map and to react accordingly. We have called these special situations relevant sensory places because (a) they capture the system’s attention and (b) they can be identified using the sensory information. To conveniently exploit the built-in models we have developed an algorithm able to make internalized plans, establishing a suggestion network in the sensory relevant places in such way that the robot can find at those places a recommended navigation direction. It has been also developed a robust navigation system able to navigate by means of interpreting and adapting the internalized plans to the concrete circumstances at each instant, i.e. a reactive navigation system. This reactive system is based on the artificial potential field approach with the additional feature introduced in the thesis of what we call fictitious charges as an aid to avoid local minima. As a general contribution of the thesis to the cognitive science field all the above described elements are integrated in a memory-based architecture, emphasizing the important role played by the memory in the cognitive processes of living beings and giving a conceptual turn in the usual process-based approach.

An approach to a model of the visual cortex: disorders of vision

One of the most challenging problems that must be solved by any theoretical model purporting to explain the competence of the human brain for relational tasks is the one related with the analysis and representation of the internal structure in an extended spatial layout of múltiple objects. In this way, some of the problems are related with specific aims as how can we extract and represent spatial relationships among objects, how can we represent the movement of a selected object and so on. The main objective of this paper is the study of some plausible brain structures that can provide answers in these problems. Moreover, in order to achieve a more concrete knowledge, our study will be focused on the response of the retinal layers for optical information processing and how this information can be processed in the first cortex layers. The model to be reported is just a first trial and some major additions are needed to complete the whole vision process.

Engineering Computational Emotion - A Reference Model for Emotion in Artificial Systems

Emotion is generally argued to be an influence on the behavior of life systems, largely concerning flexibility and adaptivity. The way in which life systems acts in response to a particular situations of the environment, has revealed the decisive and crucial importance of this feature in the success of behaviors. And this source of inspiration has influenced the way of thinking artificial systems. During the last decades, artificial systems have undergone such an evolution that each day more are integrated in our daily life. They have become greater in complexity, and the subsequent effects are related to an increased demand of systems that ensure resilience, robustness, availability, security or safety among others. All of them questions that raise quite a fundamental challenges in control design. This thesis has been developed under the framework of the Autonomous System project, a.k.a the ASys-Project. Short-term objectives of immediate application are focused on to design improved systems, and the approaching of intelligence in control strategies. Besides this, long-term objectives underlying ASys-Project concentrate on high order capabilities such as cognition, awareness and autonomy. This thesis is placed within the general fields of Engineery and Emotion science, and provides a theoretical foundation for engineering and designing computational emotion for artificial systems. The starting question that has grounded this thesis aims the problem of emotion--based autonomy. And how to feedback systems with valuable meaning has conformed the general objective. Both the starting question and the general objective, have underlaid the study of emotion, the influence on systems behavior, the key foundations that justify this feature in life systems, how emotion is integrated within the normal operation, and how this entire problem of emotion can be explained in artificial systems. By assuming essential differences concerning structure, purpose and operation between life and artificial systems, the essential motivation has been the exploration of what emotion solves in nature to afterwards analyze analogies for man--made systems. This work provides a reference model in which a collection of entities, relationships, models, functions and informational artifacts, are all interacting to provide the system with non-explicit knowledge under the form of emotion-like relevances. This solution aims to provide a reference model under which to design solutions for emotional operation, but related to the real needs of artificial systems. The proposal consists of a multi-purpose architecture that implement two broad modules in order to attend: (a) the range of processes related to the environment affectation, and (b) the range or processes related to the emotion perception-like and the higher levels of reasoning. This has required an intense and critical analysis beyond the state of the art around the most relevant theories of emotion and technical systems, in order to obtain the required support for those foundations that sustain each model. The problem has been interpreted and is described on the basis of AGSys, an agent assumed with the minimum rationality as to provide the capability to perform emotional assessment. AGSys is a conceptualization of a Model-based Cognitive agent that embodies an inner agent ESys, the responsible of performing the emotional operation inside of AGSys. The solution consists of multiple computational modules working federated, and aimed at conforming a mutual feedback loop between AGSys and ESys. Throughout this solution, the environment and the effects that might influence over the system are described as different problems. While AGSys operates as a common system within the external environment, ESys is designed to operate within a conceptualized inner environment. And this inner environment is built on the basis of those relevances that might occur inside of AGSys in the interaction with the external environment. This allows for a high-quality separate reasoning concerning mission goals defined in AGSys, and emotional goals defined in ESys. This way, it is provided a possible path for high-level reasoning under the influence of goals congruence. High-level reasoning model uses knowledge about emotional goals stability, letting this way new directions in which mission goals might be assessed under the situational state of this stability. This high-level reasoning is grounded by the work of MEP, a model of emotion perception that is thought as an analogy of a well-known theory in emotion science. The work of this model is described under the operation of a recursive-like process labeled as R-Loop, together with a system of emotional goals that are assumed as individual agents. This way, AGSys integrates knowledge that concerns the relation between a perceived object, and the effect which this perception induces on the situational state of the emotional goals. This knowledge enables a high-order system of information that provides the sustain for a high-level reasoning. The extent to which this reasoning might be approached is just delineated and assumed as future work. This thesis has been studied beyond a long range of fields of knowledge. This knowledge can be structured into two main objectives: (a) the fields of psychology, cognitive science, neurology and biological sciences in order to obtain understanding concerning the problem of the emotional phenomena, and (b) a large amount of computer science branches such as Autonomic Computing (AC), Self-adaptive software, Self-X systems, Model Integrated Computing (MIC) or the paradigm of models@runtime among others, in order to obtain knowledge about tools for designing each part of the solution. The final approach has been mainly performed on the basis of the entire acquired knowledge, and described under the fields of Artificial Intelligence, Model-Based Systems (MBS), and additional mathematical formalizations to provide punctual understanding in those cases that it has been required. This approach describes a reference model to feedback systems with valuable meaning, allowing for reasoning with regard to (a) the relationship between the environment and the relevance of the effects on the system, and (b) dynamical evaluations concerning the inner situational state of the system as a result of those effects. And this reasoning provides a framework of distinguishable states of AGSys derived from its own circumstances, that can be assumed as artificial emotion.

Quantifying epistemic actions in human-computer interaction

As digital systems move away from traditional desktop setups, new interaction paradigms are emerging that better integrate with users’ realworld surroundings, and better support users’ individual needs. While promising, these modern interaction paradigms also present new challenges, such as a lack of paradigm-specific tools to systematically evaluate and fully understand their use. This dissertation tackles this issue by framing empirical studies of three novel digital systems in embodied cognition – an exciting new perspective in cognitive science where the body and its interactions with the physical world take a central role in human cognition. This is achieved by first, focusing the design of all these systems on a contemporary interaction paradigm that emphasizes physical interaction on tangible interaction, a contemporary interaction paradigm; and second, by comprehensively studying user performance in these systems through a set of novel performance metrics grounded on epistemic actions, a relatively well established and studied construct in the literature on embodied cognition. The first system presented in this dissertation is an augmented Four-in-a-row board game. Three different versions of the game were developed, based on three different interaction paradigms (tangible, touch and mouse), and a repeated measures study involving 36 participants measured the occurrence of three simple epistemic actions across these three interfaces. The results highlight the relevance of epistemic actions in such a task and suggest that the different interaction paradigms afford instantiation of these actions in different ways. Additionally, the tangible version of the system supports the most rapid execution of these actions, providing novel quantitative insights into the real benefits of tangible systems. The second system presented in this dissertation is a tangible tabletop scheduling application. Two studies with single and paired users provide several insights into the impact of epistemic actions on the user experience when these are performed outside of a system’s sensing boundaries. These insights are clustered by the form, size and location of ideal interface areas for such offline epistemic actions to occur, as well as how can physical tokens be designed to better support them. Finally, and based on the results obtained to this point, the last study presented in this dissertation directly addresses the lack of empirical tools to formally evaluate tangible interaction. It presents a video-coding framework grounded on a systematic literature review of 78 papers, and evaluates its value as metric through a 60 participant study performed across three different research laboratories. The results highlight the usefulness and power of epistemic actions as a performance metric for tangible systems. In sum, through the use of such novel metrics in each of the three studies presented, this dissertation provides a better understanding of the real impact and benefits of designing and developing systems that feature tangible interaction.

Dissonance between conceptions of learning and ways of learning for indigenous Australian university students

This article reports on a phenomenographic investigation into conceptions of learning for 15 Indigenous Australian university students over the three years of their degree courses. The ways in which they went about learning were also investigated along with the relationship between individual students' 'core' conceptions of learning and the ways in which they learned. Results indicated that their conceptions and ways of learning were similar in some respects to those found for other university students. However, some students went about learning in ways that were incongruent with the core conception of learning they held. This can be regarded as dissonance between strategies and conceptions of learning. The implications of this for teaching and learning for such students are discussed.

Drinking refusal self-efficacy questionnaire-revised (DRSEQ-R): a new factor structure with confirmatory factor analysis

The drinking refusal self-efficacy questionnaire (DRSEQ: Young, R.M., Oei, T.P.S., 1996. Drinking expectancy profile: test manual. Behaviour Research and Therapy Centre, University of Queensland, Australia Young, R.M., Oei, T.P.S., Crook, G.M., 1991. Development of a drinking refusal self-efficacy questionnaire. J. Psychopathol. Behav. Assess., 13, 1-15) assesses a person's belief in their ability to resist alcohol. The DRSEQ is a sound psychometric instrument based on exploratory factor analyses, but has not been subjected to confirmatory factor analysis. In total 2773 participants were used to confirm the factor structure of the DRSEQ. Initial analyses revealed that the original structure was not confirmed in the current study. Subsequent analyses resulted in a revised factor structure (DRSEQ-R) being confirmed in community, student and clinical samples. The DRSEQ-R was also found to have good construct and concurrent validity. The factor structure of the DRSEQ-R is more stable than the original structure of the DRSEQ and the revised scale has considerable potential in future alcohol-related research. (c) 2004 Elsevier Ireland Ltd. All rights reserved.

Testing new alarms for medical electrical equipment

This paper reports the first of several tests of new auditory alarms originally proposed by Block et al. [1] and formalized in IEC 60601-1-8 for use in medical electrical equipment. We test whether participants who are supplied with the IEC-recommended mnemonics while learning label-alarm associations can more accurately identify the alarms after short periods of learning. Results for 18 participants strongly indicate that there is a mutual confusability between certain alarm pairs in both learning conditions, but that mnemonics may strengthen rather than diminish certain key confusions.

Dynamic facial expressions evoke distinct activation in the face perception network:a connectivity analysis study

Very little is known about the neural structures involved in the perception of realistic dynamic facial expressions. In the present study, a unique set of naturalistic dynamic facial emotional expressions was created. Through fMRI and connectivity analysis, a dynamic face perception network was identified, which is demonstrated to extend Haxby et al.'s [Haxby, J. V., Hoffman, E. A., & Gobbini, M. I. The distributed human neural system for face perception. Trends in Cognitive Science, 4, 223–233, 2000] distributed neural system for face perception. This network includes early visual regions, such as the inferior occipital gyrus, which is identified as insensitive to motion or affect but sensitive to the visual stimulus, the STS, identified as specifically sensitive to motion, and the amygdala, recruited to process affect. Measures of effective connectivity between these regions revealed that dynamic facial stimuli were associated with specific increases in connectivity between early visual regions, such as the inferior occipital gyrus and the STS, along with coupling between the STS and the amygdala, as well as the inferior frontal gyrus. These findings support the presence of a distributed network of cortical regions that mediate the perception of different dynamic facial expressions.