Cognitive architecture for an Attention-based and Bidirectional Loop-closing Domain (CABILDO)

This Ph. D. Thesis presents a novel attention-based cognitive architecture for social robots. The architecture aims to join perception and reasoning considering a double and simultaneous imbrication: the ongoing task biases the perceptual process to obtain only useful elements whereas perceived items determine the behaviours to be accomplished. Therefore, the proposed architecture represents a bidirectional solution to the perception-reasoning-action loop closing problem. The basis of the architecture is an Object-Based Visual Attention model. This perception system draws attention over perceptual units of visual information, called proto-objects. In order to highlight relevant elements, not only several intrinsic basic features (such as colour, location or shape) but also the constraints provided by the ongoing behaviour and context are considered. The proposed architecture is divided into two levels of performance. The lower level is concerned with quantitative models of execution, namely tasks that are suitable for the current work conditions, whereas a qualitative framework that describes and defines tasks relationships and coverages is placed at the top level. Perceived items determine the tasks that can be executed in each moment, following a need-based approach. Thereby, the tasks that better fit the perceived environment are more likely to be executed. Finally, the cognitive architecture has been tested using a real and unrestricted scenario that involves a real robot, time-varying tasks and daily life situations, in order to demonstrate that the proposal is able to efficiently address time- and behaviour-varying environments, overcoming the main drawbacks of already existing models

Three essays on problem-solving in collaborative open productions

The term “open production” is frequently used to describe production systems that rely on volunteer participants who are willing to participate, produce, and bear private costs in order to provide a public good. Examples of open production are becoming increasingly common in many industries. What make these productions possible? How may they be sustained in a world of organizations in which the evolutionary products of economic selection are elaborate hierarchical forms of organization? One way to address these questions is to look at how open productions solve problems that are common to all production organizations such as, for example, problems in the division of labor, allocation of tasks, collaboration, coordination, and maintaining balance between inducement and contributions. Under the conditions of extreme decentralization that are the defining feature of open productions, this approach implies a detailed observation of individual problem solving practices. This is the approach I develop in my dissertation. Unlike much of the prior literature on open productions, I deemphasize motivational elements, status-seeking motives, and allocation of property rights issues. I focus instead on actual work practices as revealed by the day-by-day problem solving activities that qualify open productions projects as production organizations despite the absence of formal contractual arrangements to regulate principal-agent relations. What my work adds to the extensive, informative, and well-developed discipline-based explanations that are currently available, is a focus on the emergence of micro-organizational mechanisms through which problem assignment (Chapter 2), problem resolution (Chapter 3), and sustained participation (Chapter 4) are obtained in open productions. In my essays, I draw from organizational sociology and the behavioral theory of the firm to specify models that relate individual problem-solving activities to structured patterns of action through emergent work practices. In the models that I specify and test, I emphasize processes of attention allocation (Chapter 2), repeated collaboration and group diversity (Chapter 3) and identity construction (Chapter 4) as central to our understanding of the dynamics of problem-solving in organizations. One element of novelty in my study is that my research design makes these work practices directly observable at a level of detail, completeness, and precision that was inaccessible in the past. To illustrate the empirical value of the view that I develop I examine problem-solving activities – i.e., bug fixing and code production – within two Free/Open Source Software (F/OSS) projects during their entire life span. Readers of my work will know more about how organizational micro-mechanisms emerge in open productions

Visual Attention in Dynamic Environments and its Application to Playing Online Games

Abstract In this thesis we present a prototype of Cognitive Programs (CPs) - an executive controller built on top of Selective Tuning (ST) model of attention. CPs enable top-down control of visual system and interaction between the low-level vision and higher-level task demands. Abstract We implement a subset of CPs for playing online video games in real time using only visual input. Two commercial closed-source games - Canabalt and Robot Unicorn Attack - are used for evaluation. Their simple gameplay and minimal controls put the emphasis on reaction speed and attention over planning. Abstract Our implementation of Cognitive Programs plays both games at human expert level, which experimentally proves the validity of the concept. Additionally we resolved multiple theoretical and engineering issues, e.g. extending the CPs to dynamic environments, finding suitable data structures for describing the task and information flow within the network and determining the correct timing for each process

Gaining Insight into Determinants of Physical Activity using Bayesian Network Learning

Contains fulltext : 228326pre.pdf (preprint version ) (Open Access) Contains fulltext : 228326pub.pdf (publisher's version ) (Open Access)BNAIC/BeneLearn 202

Architecting system of systems: artificial life analysis of financial market behavior

This research study focuses on developing a framework that can be utilized by system architects to understand the emergent behavior of system architectures. The objective is to design a framework that is modular and flexible in providing different ways of modeling sub-systems of System of Systems. At the same time, the framework should capture the adaptive behavior of the system since evolution is one of the key characteristics of System of Systems. Another objective is to design the framework so that humans can be incorporated into the analysis. The framework should help system architects understand the behavior as well as promoters or inhibitors of change in human systems. Computational intelligence tools have been successfully used in analysis of Complex Adaptive Systems. Since a System of Systems is a collection of Complex Adaptive Systems, a framework utilizing combination of these tools can be developed. Financial markets are selected to demonstrate the various architectures developed from the analysis framework --Introduction, page 3

Human Guidance Behavior Decomposition and Modeling

University of Minnesota Ph.D. dissertation. December 2017. Major: Aerospace Engineering. Advisor: Berenice Mettler. 1 computer file (PDF); x, 128 pages.Trained humans are capable of high performance, adaptable, and robust first-person dynamic motion guidance behavior. This behavior is exhibited in a wide variety of activities such as driving, piloting aircraft, skiing, biking, and many others. Human performance in such activities far exceeds the current capability of autonomous systems in terms of adaptability to new tasks, real-time motion planning, robustness, and trading safety for performance. The present work investigates the structure of human dynamic motion guidance that enables these performance qualities. This work uses a first-person experimental framework that presents a driving task to the subject, measuring control inputs, vehicle motion, and operator visual gaze movement. The resulting data is decomposed into subspace segment clusters that form primitive elements of action-perception interactive behavior. Subspace clusters are defined by both agent-environment system dynamic constraints and operator control strategies. A key contribution of this work is to define transitions between subspace cluster segments, or subgoals, as points where the set of active constraints, either system or operator defined, changes. This definition provides necessary conditions to determine transition points for a given task-environment scenario that allow a solution trajectory to be planned from known behavior elements. In addition, human gaze behavior during this task contains predictive behavior elements, indicating that the identified control modes are internally modeled. Based on these ideas, a generative, autonomous guidance framework is introduced that efficiently generates optimal dynamic motion behavior in new tasks. The new subgoal planning algorithm is shown to generate solutions to certain tasks more quickly than existing approaches currently used in robotics