Alle Publikationen
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2018
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(2018): Ethics for Robots as Experimental Technologies: Pairing Anticipation with Exploration to Evaluate the Social Impact of Robotics. In: IEEE Robotics Automation Magazine 25 (1), S. 30-36. DOI: 10.1109/MRA.2017.2781543
DOI: https://doi.org/10.1109/MRA.2017.2781543 Abstract: The evaluation of the societal impact of autonomous technologies, particularly robotics, has grown in technological contexts (e.g., see [14] and the IEEE Global Initiative for Ethical Considerations in Artificial Intelligence and Autonomous Systems [20]), as well as broader political contexts (e.g., see the 2017 European Parliament report regarding civil law rules on robotics [21]). In this article, we adopt the perspective that conceptualizes new technologies as social experiments, stressing their experimental character to deal with the inherent uncertainty that affects their behavior. We suggest that the kind of experiments performed when evaluating robots in specific contexts of use are explorative experiments, i.e., investigations carried out in the absence of a proper theory or theoretical background that diverge from the traditional notion of controlled experiments. Considering this epistemological shift, we apply the ethical framework proposed by van de Poel for experimental technologies to the case of robotics, and we discuss its implications on the design of robots. To make our discussion more concrete, we reference the field of robots for search and rescue, which offers a challenging opportunity to test socioethical approaches to the development of robots and their interactions with environments and humans.
Keywords: Artificial intelligence, autonomous robots, autonomous technologies, ethical aspects, Ethics, experimental technologies, ieee xplore, Künstliche Intelligenz, Law, Monitoring, Politics, Robotics, Robots, service robot, social aspects of automation, Social implications of technology, technological contexts -
(2018) : A Computational Framework for Integrating Task Planning and Norm Aware Reasoning for Social Robots: 2018 27th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN): Nanjing, China: IEEE Robotics & Automation Society, S. 282-287
DOI: https://doi.org/10.1109/ROMAN.2018.8525577 Abstract: Autonomous robots are envisioned to increasingly become part of our lives in the house, restaurants, hospitals and offices. Additionally, self-driving cars will be soon appearing in city streets and highways and they will have to interact with cars driven by humans as well as other self-driving cars. In these settings the robots not only need to efficiently perform their tasks but also be able to interact with humans in socially appropriate ways. To accomplish this, robots must be able to reason not only on how to perform their tasks, but also incorporate societal values, social norms and legal rules so they can gain human acceptability and trust. Moreover, interactions with these robots will be long term. Long-term human interaction with robots as well as robot combined reasoning about both tasks and social norms generate multiple modeling and computational challenges. In this paper, we address one of the most important of these challenges, namely what is an appropriate and scalable computational framework that enables simultaneous task and normative reasoning. In particular, we report on our work on a novel computational framework, Modular Normative Markov Decision Processes (MNMDP) that integrates reasoning for domain tasks and normative reasoning for long-term autonomy. The MNMDP framework applies normative reasoning considering only the norms that are activated in appropriate contexts, rather than considering the full set of norms, thus significantly reducing computational complexity. The model modularity is also advantageous for long-term human-robot interaction. We present computational experiments that show significant computational improvements as compared with a base Normative Markov Decision Process (MDP) framework that includes the full set of norms.
Keywords: Angemessen(heit) (von Technik), Autonomous automobiles, autonomous robots, Cognition, Computational complexity, Decision theory, human acceptability, human-robot interaction, ieee xplore, inference mechanisms, knowledge based systems, long-term human interaction, Markov processes, MNMDP framework, Mobile robots, modular normative Markov decision processes, norm aware reasoning, normative Markov decision process framework, Normative reasoning, path planning, Planning, robot combined reasoning, self-driving cars, Social Norms, social robots, societal values, Task Analysis, task planning 2017
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(2017): Fear of autonomous robots and artificial intelligence: Evidence from national representative data with probability sampling. In: International Journal of Social Robotics 9 (3), S. 379-384. DOI: 10.1007/s12369-017-0401-3
DOI: https://doi.org/10.1007/s12369-017-0401-3 Abstract: People vary in the extent to which they report fear toward robots, especially when they perceive that the robot is autonomous or has artificial intelligence. This research examines a specific form of sociological fear, which we name as fear of autonomous robots and artificial intelligence (FARAI). This fear may serve to affect how people will respond to and interact with robots. Applying data from a nationally representative dataset with probability sampling (N = 1541), research questions examine (1) the extent and frequency of FARAI, (2) demographic and media exposure predictors, and (3) correlates with other types of fear (i.e., loneliness, drones, and unemployment). A latent class analysis reveals that approximately 26% of participants reported experiencing a heightened level of FARAI. Demographic analyses show that FARAI is connected to participant sex, age, education, and household income; albeit these effects were small. Media exposure to science fiction predicts FARAI above and beyond the demographic variables. Correlational results indicate that FARAI is associated with other types of fear, including loneliness, becoming unemployed, and drone use. In sum, these findings render a much needed glimpse and update regarding how much individuals fear robots and artificial intelligence. (PsycINFO Database Record (c) 2017 APA, all rights reserved)
2016
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(2016): Outline of a sensory-motor perspective on intrinsically moral agents. In: Adaptive Behavior 24 (5), S. 306-319. DOI: 10.1177/1059712316667203
DOI: https://doi.org/10.1177/1059712316667203 Abstract: We propose that moral behaviour of artificial agents could (and should) be intrinsically grounded in their own sensory-motor experiences. Such an ability depends critically on seven types of competencies. First, intrinsic morality should be grounded in the internal values of the robot arising from its physiology and embodiment. Second, the moral principles of robots should develop through their interactions with the environment and with other agents. Third, we claim that the dynamics of moral (or social) emotions closely follows that of other non-social emotions used in valuation and decision making. Fourth, we explain how moral emotions can be learned from the observation of others. Fifth, we argue that to assess social interaction, a robot should be able to learn about and understand responsibility and causation. Sixth, we explain how mechanisms that can learn the consequences of actions are necessary for a robot to make moral decisions. Seventh, we describe how the moral evaluation mechanisms outlined can be extended to situations where a robot should understand the goals of others. Finally, we argue that these competencies lay the foundation for robots that can feel guilt, shame and pride, that have compassion and that know how to assign responsibility and blame. (PsycINFO Database Record (c) 2017 APA, all rights reserved)
2012
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(2012): Moral Decision Making in Autonomous Systems: Enforcement, Moral Emotions, Dignity, Trust, and Deception. In: Proceedings of the IEEE 100 (3), S. 571-589. DOI: 10.1109/JPROC.2011.2173265
DOI: https://doi.org/10.1109/JPROC.2011.2173265 Abstract: As humans are being progressively pushed further downstream in the decision-making process of autonomous systems, the need arises to ensure that moral standards, however defined, are adhered to by these robotic artifacts. While meaningful inroads have been made in this area regarding the use of ethical lethal military robots, including work by our laboratory, these needs transcend the warfighting domain and are pervasive, extending to eldercare, robot nannies, and other forms of service and entertainment robotic platforms. This paper presents an overview of the spectrum and specter of ethical issues raised by the advent of these systems, and various technical results obtained to date by our research group, geared towards managing ethical behavior in autonomous robots in relation to humanity. This includes: 1) the use of an ethical governor capable of restricting robotic behavior to predefined social norms; 2) an ethical adaptor which draws upon the moral emotions to allow a system to constructively and proactively modify its behavior based on the consequences of its actions; 3) the development of models of robotic trust in humans and its dual, deception, drawing on psychological models of interdependence theory; and 4) concluding with an approach towards the maintenance of dignity in human-robot relationships.
Keywords: Adaptation models, Autonomic systems, autonomous robots, autonomous systems, Behavioral science, Decision Making, entertainment robotic platforms, ethical adaptor, ethical aspects, ethical governor, ethical issues, ethical lethal military robots, Ethics, Human Computer Interaction, Human Factors, human-robot relationships, ieee xplore, Moral & Ethik, moral decision making, Moral emotions, Robot Ethics, robot nannies, robotic artifacts, Robots, service robot, Social implications of technology, unmanned systems 2008
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(2008): On the design of neuro-controllers for individual and social learning behaviour in autonomous robots. An evolutionary approach. In: Connection Science 20 (2-3), S. 211-230. DOI: 10.1080/09540090802092014
Abstract: In biology/psychology, the capability of natural organisms to learn from the observation/interaction with conspecifics is referred to as social learning. Roboticists have recently developed an interest in social learning, since it might represent an effective strategy to enhance the adaptivity of a team of autonomous robots. In this study, we show that a methodological approach based on artifcial neural networks shaped by evolutionary computation techniques can be successfully employed to synthesise the individual and social learning mechanisms for robots required to learn a desired action (i.e. phototaxis or antiphototaxis).
1995
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(1995): Getting to know each other—Artificial social intelligence for autonomous robots. In: Robotics and Autonomous Systems 16 (2), S. 333-356. DOI: 10.1016/0921-8890(95)00054-2
DOI: https://doi.org/10.1016/0921-8890(95)00054-2 Abstract: This paper proposes a research direction to study the development of ‘artificial social intelligence’ of autonomous robots which should result in ‘individualized robot societies’. The approach is highly inspired by the ‘social intelligence hypothesis’, derived from the investigation of primate societies, suggesting that primate intelligence originally evolved to solve social problems and was only later extended to problems outside the social domain. We suggest that it might be a general principle in the evolution of intelligence, applicable to both natural and artificial systems. Arguments are presented why the investigation of social intelligence for artifacts is not only an interesting research issue for the study of biological principles, but may be a necessary prerequisite for those scenarios in which autonomous robots are integrated into human societies, interacting and communicating both with humans and with each other. As a starting point to study experimentally the development of robots' ‘social relationships’, the investigation of collection and use of body images by means of imitation is proposed. A specific experimental setup which we use to test the theoretical considerations is described. The paper outlines in what kind of applications and for what kind of robot group structures social intelligence might be advantageous.
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