The RACE Project: Robustness by Autonomous Competence Enhancement

This paper reports on the aims, the approach, and the results of the European project RACE. The project aim was to enhance the behavior of an autonomous robot by having the robot learn from conceptualized experiences of previous performance, based on initial models of the domain and its own actions in it. This paper introduces the general system architecture; it then sketches some results in detail regarding hybrid reasoning and planning used in RACE, and instances of learning from the experiences of real robot task execution. Enhancement of robot competence is operationalized in terms of performance quality and description length of the robot instructions, and such enhancement is shown to result from the RACE system

Public Opinions of Unmanned Aerial Technologies in 2014 to 2019: A Technical and Descriptive Report

The primary purpose of this report is to provide a descriptive and technical summary of the results from similar surveys administered in fall 2014 (n = 576), 2015 (n = 301), 2016 (ns = 1946 and 2089), and 2018 (n = 1050) and summer 2019 (n = 1300). In order to explore a variety of factors that may impact public perceptions of unmanned aerial technologies (UATs), we conducted survey experiments over time. These experiments randomly varied the terminology (drone, aerial robot, unmanned aerial vehicle (UAV), unmanned aerial system (UAS)) used to describe the technology, the purposes of the technology (for economic, environmental, or security goals), the actors (public or private) using the technology, the technology’s autonomy (fully autonomous, partially autonomous, no autonomy), and the framing (promotion or prevention) used to describe the technology’s purpose. Initially, samples were recruited through Amazon’s Mechanical Turk, required to be Americans, and paid a small amount for participation. In 2016 we also examined a nationally representative samples recruited from Qualtrics panels. After 2016 we only used nationally representative samples from Qualtrics. Major findings are reported along with details regarding the research methods and analyses

Eye quietness and quiet eye in expert and novice golf performance: an electrooculographic analysis

Quiet eye (QE) is the final ocular fixation on the target of an action (e.g., the ball in golf putting). Camerabased eye-tracking studies have consistently found longer QE durations in experts than novices; however, mechanisms underlying QE are not known. To offer a new perspective we examined the feasibility of measuring the QE using electrooculography (EOG) and developed an index to assess ocular activity across time: eye quietness (EQ). Ten expert and ten novice golfers putted 60 balls to a 2.4 m distant hole. Horizontal EOG (2ms resolution) was recorded from two electrodes placed on the outer sides of the eyes. QE duration was measured using a EOG voltage threshold and comprised the sum of the pre-movement and post-movement initiation components. EQ was computed as the standard deviation of the EOG in 0.5 s bins from –4 to +2 s, relative to backswing initiation: lower values indicate less movement of the eyes, hence greater quietness. Finally, we measured club-ball address and swing durations. T-tests showed that total QE did not differ between groups (p = .31); however, experts had marginally shorter pre-movement QE (p = .08) and longer post-movement QE (p < .001) than novices. A group × time ANOVA revealed that experts had less EQ before backswing initiation and greater EQ after backswing initiation (p = .002). QE durations were inversely correlated with EQ from –1.5 to 1 s (rs = –.48 - –.90, ps = .03 - .001). Experts had longer swing durations than novices (p = .01) and, importantly, swing durations correlated positively with post-movement QE (r = .52, p = .02) and negatively with EQ from 0.5 to 1s (r = –.63, p = .003). This study demonstrates the feasibility of measuring ocular activity using EOG and validates EQ as an index of ocular activity. Its findings challenge the dominant perspective on QE and provide new evidence that expert-novice differences in ocular activity may reflect differences in the kinematics of how experts and novices execute skills

Playing the trump card:Why we select overconfident leaders and why it matters

Five studies test the relationship between overconfidence and perceived leadership suitability. Study 1, a field study wherein HR consultants assessed candidates for an advertised leadership position, finds that overconfidence positively predicts hiring recommendations. Study 2, in which participants delivered a five-minute job talk to an expert panel, finds that overconfidence buffers social stress, thereby improving participants' job pitches. Study 3, which tested the effect of confidence on leadership selection at different levels of manipulated competence, finds that regardless of competence, confidence increases perceived leadership potential. Study 4, finds that within the context of the 2016 US Primaries, voters were swayed by candidates' confidence, regardless of candidate competence. Study 5, an agent-based simulation, demonstrates that if candidates adjust to voter preferences for confidence, competent candidates become less likely to be elected. These findings suggest that overconfidence manifests behavioral displays that activate people's implicit leadership theories, thereby increasing perceptions of leadership potential

How Europe can deliver: Optimising the division of competences among the EU and its member states

This study aims to give guidance for a better-performing EU through an improved allocation of competences between the European Union and its member states. The study analyses eight specific policies from a wide range of fields with respect to their preferable assignment. The analysis applies a unified quantified approach and is precise in its definition of ‘counterfactuals’. These counterfactuals are understood as conceptual alternatives to the allocation of competences under the status quo. As such, they either relate to a new European competence (if the policy is currently a national responsibility) or a new national competence (if the policy is currently assigned to the EU). The comprehensive, quantification-based assessments indicate that it would be preferable to have responsibility for higher education and providing farmers with income support at the national level. Conversely, a shift of competences to the EU level would be advantageous when it comes to asylum policies, defence, corporate taxation, development aid and a (complementary) unemployment insurance scheme in the euro area. For one policy – railway freight transport – the findings are indeterminate. Overall, the study recommends a differentiated integration strategy comprising both new European policies and a roll-back of EU competences in other fields

Grounding language in perception for scene conceptualization in autonomous robots

In order to behave autonomously, it is desirable for robots to have the ability to use human supervision and learn from different input sources (perception, gestures, verbal and textual descriptions etc). In many machine learning tasks, the supervision is directed specifically towards machines and hence is straight forward clearly annotated examples. But this is not always very practical and recently it was found that the most preferred interface to robots is natural language. Also the supervision might only be available in a rather indirect form, which may be vague and incomplete. This is frequently the case when humans teach other humans since they may assume a particular context and existing world knowledge. We explore this idea here in the setting of conceptualizing objects and scene layouts. Initially the robot undergoes training from a human in recognizing some objects in the world and armed with this acquired knowledge it sets out in the world to explore and learn more higher level concepts like static scene layouts and environment activities. Here it has to exploit its learned knowledge and ground language into perception to use inputs from different sources that might have overlapping as well as novel information. When exploring, we assume that the robot is given visual input, without explicit type labels for objects, and also that it has access to more or less generic linguistic descriptions of scene layout. Thus our task here is to learn the spatial structure of a scene layout and simultaneously visual object models it was not trained on. In this paper, we present a cognitive architecture and learning framework for robot learning through natural human supervision and using multiple input sources by grounding language in perception

Recolha e conceptualização de experiências de atividades robóticas baseadas em planos para melhoria de competências no longo prazo

Robot learning is a prominent research direction in intelligent robotics. Robotics involves dealing with the issue of integration of multiple technologies, such as sensing, planning, acting, and learning. In robot learning, the long term goal is to develop robots that learn to perform tasks and continuously improve their knowledge and skills through observation and exploration of the environment and interaction with users. While significant research has been performed in the area of learning motor behavior primitives, the topic of learning high-level representations of activities and classes of activities that, decompose into sequences of actions, has not been sufficiently addressed. Learning at the task level is key to increase the robots’ autonomy and flexibility. High-level task knowledge is essential for intelligent robotics since it makes robot programs less dependent on the platform and eases knowledge exchange between robots with different kinematics. The goal of this thesis is to contribute to the development of cognitive robotic capabilities, including supervised experience acquisition through human-robot interaction, high-level task learning from the acquired experiences, and task planning using the acquired task knowledge. A framework containing the required cognitive functions for learning and reproduction of high-level aspects of experiences is proposed. In particular, we propose and formalize the notion of Experience-Based Planning Domains (EBPDs) for long-term learning and planning. A human-robot interaction interface is used to provide a robot with step-by-step instructions on how to perform tasks. Approaches to recording plan-based robot activity experiences including relevant perceptions of the environment and actions taken by the robot are presented. A conceptualization methodology is presented for acquiring task knowledge in the form of activity schemata from experiences. The conceptualization approach is a combination of different techniques including deductive generalization, different forms of abstraction and feature extraction. Conceptualization includes loop detection, scope inference and goal inference. Problem solving in EBPDs is achieved using a two-layer problem solver comprising an abstract planner, to derive an abstract solution for a given task problem by applying a learned activity schema, and a concrete planner, to refine the abstract solution towards a concrete solution. The architecture and the learning and planning methods are applied and evaluated in several real and simulated world scenarios. Finally, the developed learning methods are compared, and conditions where each of them has better applicability are discussed.Aprendizagem de robôs é uma direção de pesquisa proeminente em robótica inteligente. Em robótica, é necessário lidar com a questão da integração de várias tecnologias, como percepção, planeamento, atuação e aprendizagem. Na aprendizagem de robôs, o objetivo a longo prazo é desenvolver robôs que aprendem a executar tarefas e melhoram continuamente os seus conhecimentos e habilidades através da observação e exploração do ambiente e interação com os utilizadores. A investigação tem-se centrado na aprendizagem de comportamentos básicos, ao passo que a aprendizagem de representações de atividades de alto nível, que se decompõem em sequências de ações, e de classes de actividades, não tem sido suficientemente abordada. A aprendizagem ao nível da tarefa é fundamental para aumentar a autonomia e a flexibilidade dos robôs. O conhecimento de alto nível permite tornar o software dos robôs menos dependente da plataforma e facilita a troca de conhecimento entre robôs diferentes. O objetivo desta tese é contribuir para o desenvolvimento de capacidades cognitivas para robôs, incluindo aquisição supervisionada de experiência através da interação humano-robô, aprendizagem de tarefas de alto nível com base nas experiências acumuladas e planeamento de tarefas usando o conhecimento adquirido. Propõe-se uma abordagem que integra diversas funcionalidades cognitivas para aprendizagem e reprodução de aspetos de alto nível detetados nas experiências acumuladas. Em particular, nós propomos e formalizamos a noção de Domínio de Planeamento Baseado na Experiência (Experience-Based Planning Domain, or EBPD) para aprendizagem e planeamento num âmbito temporal alargado. Uma interface para interação humano-robô é usada para fornecer ao robô instruções passo-a-passo sobre como realizar tarefas. Propõe-se uma abordagem para extrair experiências de atividades baseadas em planos, incluindo as percepções relevantes e as ações executadas pelo robô. Uma metodologia de conceitualização é apresentada para a aquisição de conhecimento de tarefa na forma de schemata a partir de experiências. São utilizadas diferentes técnicas, incluindo generalização dedutiva, diferentes formas de abstracção e extração de características. A metodologia inclui detecção de ciclos, inferência de âmbito de aplicação e inferência de objetivos. A resolução de problemas em EBPDs é alcançada usando um sistema de planeamento com duas camadas, uma para planeamento abstrato, aplicando um schema aprendido, e outra para planeamento detalhado. A arquitetura e os métodos de aprendizagem e planeamento são aplicados e avaliados em vários cenários reais e simulados. Finalmente, os métodos de aprendizagem desenvolvidos são comparados e as condições onde cada um deles tem melhor aplicabilidade são discutidos.Programa Doutoral em Informátic

Towards European Anticipatory Governance for Artificial Intelligence

This report presents the findings of the Interdisciplinary Research Group “Responsibility: Machine Learning and Artificial Intelligence” of the Berlin-Brandenburg Academy of Sciences and Humanities and the Technology and Global Affairs research area of DGAP. In September 2019, they brought leading experts from research and academia together with policy makers and representatives of standardization authorities and technology organizations to set framework conditions for a European anticipatory governance regime for artificial intelligence (AI)

Organizing for innovation: R&D projects, activities and partners

We explore how R&D project characteristics condition the governance of an R&D project and its individual activities. Prior literature has tried to understand the factors - both at the industry and at the firm level - that influence the way in which firms partner for innovation. In this paper, through the analysis of detailed data from a subsidiary of STMicroelectronics, we identify the main drivers of partner selection for innovation. Partnering or contracting with universities for innovation is common practice for developing new -original- knowledge, as opposed to applying existing knowledge to a problem. But firms are more reluctant to partner, especially with other firms, when that knowledge directly enhances their competitiveness. However, conditional on cooperation, partners are more likely to act individually when the project is strategically important. Contracting for innovation to universities or research centers, as opposed to partnering, happens for more experimental projects, where highly original knowledge is developed, and typically early on in the project.Innovation strategy; Technological innovation; R&D projects' organization; Partner selection;