Ontology for autonomous robotics

Creating a standard for knowledge representation and reasoning in autonomous robotics is an urgent task if we consider recent advances in robotics as well as predictions about the insertion of robots in human daily life. Indeed, this will impact the way information is exchanged between multiple robots or between robots and humans and how they can all understand it without ambiguity. Indeed, Human Robot Interaction (HRI) represents the interaction of at least two cognition models (Human and Robot). Such interaction informs task composition, task assignment, communication, cooperation and coordination in a dynamic environment, requiring a flexible representation. Hence, this paper presents the IEEE RAS Autonomous Robotics (AuR) Study Group, which is a spin-off of the IEEE Ontologies for Robotics and Automation (ORA) Working Group, and its ongoing work to develop the first IEEE-RAS ontology standard for autonomous robotics. In particular, this paper reports on the current version of the ontology for autonomous robotics as well as on its first implementation successfully validated for a human-robot interaction scenario, demonstrating the developed ontology’s strengths which include semantic interoperability and capability to relate ontologies from different fields for knowledge sharing and interactions.info:eu-repo/semantics/publishedVersio

Active visual search in non-stationary scenes: coping with temporal variability and uncertainty

Objective. State-of-the-art experiments for studying neural processes underlying visual cognition often constrain sensory inputs (e.g., static images) and our behavior (e.g., fixed eye-gaze, long eye fixations), isolating or simplifying the interaction of neural processes. Motivated by the non-stationarity of our natural visual environment, we investigated the electroencephalography (EEG) correlates of visual recognition while participants overtly performed visual search in non-stationary scenes. We hypothesized that visual effects (such as those typically used in human–computer interfaces) may increase temporal uncertainty (with reference to fixation onset) of cognition-related EEG activity in an active search task and therefore require novel techniques for single-trial detection. Approach. We addressed fixation-related EEG activity in an active search task with respect to stimulus-appearance styles and dynamics. Alongside popping-up stimuli, our experimental study embraces two composite appearance styles based on fading-in, enlarging, and motion effects. Additionally, we explored whether the knowledge obtained in the pop-up experimental setting can be exploited to boost the EEG-based intention-decoding performance when facing transitional changes of visual content. Main results. The results confirmed our initial hypothesis that the dynamic of visual content can increase temporal uncertainty of the cognition-related EEG activity in active search with respect to fixation onset. This temporal uncertainty challenges the pivotal aim to keep the decoding performance constant irrespective of visual effects. Importantly, the proposed approach for EEG decoding based on knowledge transfer between the different experimental settings gave a promising performance. Significance. Our study demonstrates that the non-stationarity of visual scenes is an important factor in the evolution of cognitive processes, as well as in the dynamic of ocular behavior (i.e., dwell time and fixation duration) in an active search task. In addition, our method to improve single-trial detection performance in this adverse scenario is an important step in making brain–computer interfacing technology available for human–computer interaction applications.EC/FP7/611570/EU/Symbiotic Mind Computer Interaction for Information Seeking/MindSeeBMBF, 01GQ0850, Bernstein Fokus Neurotechnologie - Nichtinvasive Neurotechnologie für Mensch-Maschine Interaktio

Interactions in Visualizations to Support Knowledge Activation

Humans have several exceptional abilities, one of which is the perceptual tasks of their visual sense. Humans have the unique ability to perceive data and identify patterns, trends, and outliers. This research investigates the design of interactive visualizations to identify the benefits of interacting with information. The research question leading the investigation is how does interacting with visualizations support analytical reasoning of emergent information to activate knowledge? The study uses the theory of distributed cognition and human-information interaction to apply the design science research framework. The motivation behind the research is to identify guidelines for interactive visualizations to enhance a user’s ability to make decisions in dynamic situations and apply knowledge gleaned from the visualization. An experiment is used to analyze the use of an interactive dashboard in a dynamic decision-making situation. The results of this experiment specifically look at the combination of interactions as they support the distribution of cognition over three spaces of a human-visualization cognitive system. The results provide insight into the benefits that interactions have for enhancing analytical reasoning, expanding the use of visualizations beyond communicating or disseminating information. Providing a broad range of interactions that work with multiple views of information increases the opportunities that users have to complete tasks. This research contributes to the information visualization discipline by expanding the focus from representing data to representing and interacting with information. Secondly, my results provide an example of a qualitative assessment based on the value of visualization, in comparison to traditional usability assessment

The challenge of complexity for cognitive systems

Complex cognition addresses research on (a) high-level cognitive processes – mainly problem solving, reasoning, and decision making – and their interaction with more basic processes such as perception, learning, motivation and emotion and (b) cognitive processes which take place in a complex, typically dynamic, environment. Our focus is on AI systems and cognitive models dealing with complexity and on psychological findings which can inspire or challenge cognitive systems research. In this overview we first motivate why we have to go beyond models for rather simple cognitive processes and reductionist experiments. Afterwards, we give a characterization of complexity from our perspective. We introduce the triad of cognitive science methods – analytical, empirical, and engineering methods – which in our opinion have all to be utilized to tackle complex cognition. Afterwards we highlight three aspects of complex cognition – complex problem solving, dynamic decision making, and learning of concepts, skills and strategies. We conclude with some reflections about and challenges for future research

Fundamental Principles of Neural Organization of Cognition

The manuscript advances a hypothesis that there are few fundamental principles of neural organization of cognition, which explain several wide areas of the cognitive functioning. We summarize the fundamental principles, experimental, theoretical, and modeling evidence for these principles, relate them to hypothetical neural mechanisms, and made a number of predictions. We consider cognitive functioning including concepts, emotions, drives-instincts, learning, “higher” cognitive functions of language, interaction of language and cognition, role of emotions in this interaction, the beautiful, sublime, and music. Among mechanisms of behavior we concentrate on internal actions in the brain, learning and decision making. A number of predictions are made, some of which have been previously formulated and experimentally confirmed, and a number of new predictions are made that can be experimentally tested. Is it possible to explain a significant part of workings of the mind from a few basic principles, similar to how Newton explained motions of planets? This manuscript summarizes a part of contemporary knowledge toward this goal

Towards a framework for investigating tangible environments for learning

External representations have been shown to play a key role in mediating cognition. Tangible environments offer the opportunity for novel representational formats and combinations, potentially increasing representational power for supporting learning. However, we currently know little about the specific learning benefits of tangible environments, and have no established framework within which to analyse the ways that external representations work in tangible environments to support learning. Taking external representation as the central focus, this paper proposes a framework for investigating the effect of tangible technologies on interaction and cognition. Key artefact-action-representation relationships are identified, and classified to form a structure for investigating the differential cognitive effects of these features. An example scenario from our current research is presented to illustrate how the framework can be used as a method for investigating the effectiveness of differential designs for supporting science learning

Self-directedness, integration and higher cognition

In this paper I discuss connections between self-directedness, integration and higher cognition. I present a model of self-directedness as a basis for approaching higher cognition from a situated cognition perspective. According to this model increases in sensorimotor complexity create pressure for integrative higher order control and learning processes for acquiring information about the context in which action occurs. This generates complex articulated abstractive information processing, which forms the major basis for higher cognition. I present evidence that indicates that the same integrative characteristics found in lower cognitive process such as motor adaptation are present in a range of higher cognitive process, including conceptual learning. This account helps explain situated cognition phenomena in humans because the integrative processes by which the brain adapts to control interaction are relatively agnostic concerning the source of the structure participating in the process. Thus, from the perspective of the motor control system using a tool is not fundamentally different to simply controlling an arm