Alle Publikationen
2019
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(2019): ARS: Eine technische Anwendung von psychoanalytischen Grundprinzipien für die Robotik und Automatisierungstechnik (UB Bielefeld - Katalog.plus!), S. 57-116. Online verfügbar unter https://katalogplus.ub.uni-bielefeld.de/cgi-bin/new_titel.cgi?katkey=0272574 pdx&query=technische%20anwendung%20von%20psychoanalytischen%20grundprinzipien%20f%C3%BCr%20die%20robotik%20und%20automatisierungstechnik&vr=1&pagesize=10&sprache=GER&bestand=ext&sess=9b28977aeae07f565aada251ca270e9d, zuletzt geprüft am 31.07.2019Keywords: Künstliche Intelligenz
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(2019): Anthropomorphization of artificial agents leads to fair and strategic, but not altruistic behavior. In: International Journal of Human-Computer Studies 122, S. 168-173. DOI: 10.1016/j.ijhcs.2018.09.008
DOI: https://doi.org/10.1016/j.ijhcs.2018.09.008 Abstract: With robots playing an increasing role in our daily lives, our emotional responses to them have become an active subject of study. The process of anthropomorphization, ascribing human affordances to non-human objects, is thought to play a large role in human-robot interaction. However, earlier studies have relied largely on experimenter’s manipulation of anthropomorphism, and the use of virtual robots. The aim of this study was to investigate peopleâs fairness preference and strategic and altruistic behavior toward different opponents (a human, a semi-humanoid and a spider-like robot, and a laptop) in two economic games. Anthropomorphization questionnaires and mood measures were also administered. Our findings suggest that fairness preference and strategic behavior are not predicted by the opponent’s physical appearance, but instead predicted by individual differences in the tendency to anthropomorphize others. Altruistic behavior, on the other hand, is affected by the opponent’s physical appearance. (PsycINFO Database Record (c) 2019 APA, all rights reserved)
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(2019) : Beyond Programming: Can Robots’ Norm-Violating Actions Elicit Mental State Attributions?: 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI): Daegu, Korea: IEEE, S. 530-531
DOI: https://doi.org/10.1109/HRI.2019.8673293 Abstract: Social perceivers often view a human agent’s norm-violating behavior as diagnostic of that person’s mental states, while behaviors that conform to norms are viewed as less informative. We developed a series of stimulus videos depicting a DRC-HUBO robot engaging in norm-violating and norm-conforming behaviors. We explored the hypothesis that robots’ norm-violating actions may invite social perceivers to increase their mental state attributions in a similar manner as they do in humans. Surprisingly, we found that norm-conforming behaviors appear to be at least as conducive as norm-violating behaviors, and perhaps even moreso, to mental state attribution to robotic agents.
Keywords: action explanation, actions elicit mental state attributions, agency, Artificial intelligence, behavioural sciences computing, Cognition, control engineering computing, DRC-HUBO, DRC-HUBO robot, human agent norm-violating behavior, Humanoid Robots, human-robot interaction, ieee xplore, Künstliche Intelligenz, Mobile robots, multi-agent systems, norms, PSYCHOLOGY, robot programming, robotic agents, social perceivers, theory of mind, Videos -
(2019): Trust in socially assistive robots: Considerations for use in rehabilitation. In: Neuroscience & Biobehavioral Reviews 104, S. 231-239. DOI: 10.1016/j.neubiorev.2019.07.014
DOI: https://doi.org/10.1016/j.neubiorev.2019.07.014 Abstract: Incorporation of social robots into rehabilitation calls for understanding what factors affect user motivation and success of the interaction. Trust between the user and the robot has been identified as important in human-robot interaction and in human-human interactions in therapy. Trust has been studied in the context of automation technology, (e.g., autonomous cars), but not in the context of social robots for rehabilitation. In this narrative review, we address the unique patient-clinician-robot triad, and argue that this context calls for specific design features in order to foster trust with the users. We review pertinent methods for measuring trust, and studies demonstrating that culture, prior experience and propensity-to-trust affect to what extent users trust robots. We suggest design guidelines for fostering trust and methods for measuring trust in human-robot interactions in rehabilitation. We stress the need to create measures of trust that are accessible to people who suffer from speech or cognitive impairments. This review is pertinent to researchers, roboticists, and clinicians interested in designing and using social robots for rehabilitation.
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(2019) : User Experience for Social Human-Robot Interactions: 2019 Amity International Conference on Artificial Intelligence (AICAI): Dubai, United Arab Emirates: IEEE, S. 32-36
DOI: https://doi.org/10.1109/AICAI.2019.8701332 Abstract: A significant threat social robots often faces is that their integration in real social, human environments will dehumanise some of the roles currently being played by the humans. This perception implicitly overestimates the social skills of the robots, which despite being continually upgraded, are still far from being able to dominate humans entirely. It also reflects loosely fears that robots may overcome humans in the near future and impact on the need to employ humans. This paper aims to address the role and relevance of user experience of socially interactive robots, separating several issues related to the evaluation of social human-robot interaction and then more specifically how this should be considered in developing countries where socially interactive robots are viewed with resistance and apprehension.
Keywords: Developing Countries, human environments, human-robot interaction, ieee xplore, Künstliche Intelligenz, Mobile robots, Robot sensing systems, service robot, Social Environments, social human-robot interaction, social robots, Social Skills, socially interactive robots, User acceptance, user experience 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): Artificial unintelligence. How computers misunderstand the world. Cambridge, Massachusetts: The MIT Press
Abstract: A guide to understanding the inner workings and outer limits of technology and why we should never assume that computers always get it right. In Artificial Unintelligence, Meredith Broussard argues that our collective enthusiasm for applying computer technology to every aspect of life has resulted in a tremendous amount of poorly designed systems. We are so eager to do everything digitally—hiring, driving, paying bills, even choosing romantic partners—that we have stopped demanding that our technology actually work. Broussard, a software developer and journalist, reminds us that there are fundamental limits to what we can (and should) do with technology. With this book, she offers a guide to understanding the inner workings and outer limits of technology—and issues a warning that we should never assume that computers always get things right. Making a case against technochauvinism—the belief that technology is always the solution—Broussard argues that it's just not true that social problems would inevitably retreat before a digitally enabled Utopia. To prove her point, she undertakes a series of adventures in computer programming. She goes for an alarming ride in a driverless car, concluding “the cyborg future is not coming any time soon”; uses artificial intelligence to investigate why students can't pass standardized tests; deploys machine learning to predict which passengers survived the Titanic disaster; and attempts to repair the U.S. campaign finance system by building AI software. If we understand the limits of what we can do with technology, Broussard tells us, we can make better choices about what we should do with it to make the world better for everyone.
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(2018): Cognitive assistants. In: International Journal of Human-Computer Studies 117, S. 1-3. DOI: 10.1016/j.ijhcs.2018.05.008
DOI: https://doi.org/10.1016/j.ijhcs.2018.05.008 Abstract: The article presents the most recent advances in the field of cognitive assistants (CA). These days CA technology is being extended to what is sometimes called 'smart advisors', which provide a universal human-centric computer information solution. Smart advisors combine generic decision support techniques with context-awareness and personalized recommendation using machine learning. They aim to help people in their daily activities in a general sense. Today, a number of prototypes of such systems exist. Some of them are well integrated into online services, as well as mobile devices. Others are embedded into dedicated robot prototypes, often oriented on care taking. Recently a more general concept of companion technologies has been introduced. The articles contained in this special issue are very heterogeneous in terms of scope, region and are by people that are a reference in this field. Five articles compose this Special Issue, and they are distributed globally, with contributions from the following countries: Germany, New Zealand, Spain, Taiwan, and United States of America. This shows the interest of academia in the CA area and the level of development that is currently underway, and provide different perspectives related to their own culture. (PsycINFO Database Record (c) 2018 APA, all rights reserved)
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(2018) : Self-integrating Organic Control Systems: from Crayfish to Smart Homes In: ARCS: ARCS Workshop 2018; 31th International Conference on Architecture of Computing Systems: Berlin, Germany: Axel Springer SE, S. 1-8. Online verfügbar unter ARCS Workshop 2018; 31th International Conference on Architecture of Computing Systems
Abstract: Survival in complex environments, for both natural and artificial systems, requires behavioural adaptation to common changes and behavioural innovation to face the unexpected. The challenge here is to produce a vast variety of behaviours, each adapted to current circumstances, while relying on a limited amount of resources (e.g. sensors, controllers and actuators), within a 'suitable' time-frame. Drawing inspiration from neural and behavioural studies on crayfish, this position paper brings to the fore several key design features that enable organisms to address this challenge. It then proposes a similar design for artificial controllers, based on: i) an extensible set of reusable control units; and, ii) a goal-driven, context-sensitive (self-)integration process for assembling control units into a wide variety of integrated system controllers. Pre-integrated sub-controllers can also be merged, to improve efficiency while avoiding conflicts. The proposal is illustrated via a proof-of-concept implementation for the smart home, where users can add and remove goals and devices at runtime and the controller is adapted accordingly. This study brings us closer to our long-term objective of defining reusable methodologies and platforms for the development of self-* systems running in complex unpredictable environments, notably including smart homes, cities, vehicular networks and electrical grids, merged via the Internet of Things, and of People.
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(2018) : Towards Self-Explaining Digital Systems: A Design Methodology for the Next Generation: 2018 IEEE 3rd International Verification and Security Workshop (IVSW): Costa Brava, Spain: IEEE, S. 1-6
DOI: https://doi.org/10.1109/IVSW.2018.8494900 Abstract: As digital systems get ever more complex, their behaviour may at times appear unfathomable. Users will only be prepared to accept this if they are convinced that the system does indeed work correctly. Thus, we argue the need for self-explaining systems: systems that are able to explain their behaviour, and the reasons for it. In this paper, we propose first steps towards a design methodology for such systems, and argue that beyond user acceptance, self-explanation also has other applications such as self-verification and reconfiguration. We propose a conceptual framework for self-explaining systems, discuss how to achieve completeness, and consider implementation aspects.
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(2018): Effects of Robot Facial Characteristics and Gender in Persuasive Human-Robot Interaction. In: Front. Robot. AI 5. DOI: 10.3389/frobt.2018.00073
DOI: https://www.frontiersin.org/article/10.3389/frobt.2018.00073/full Abstract: The growing interest in social robotics makes it relevant to examine the potential of robots as persuasive agents and, more specifically, to examine how robot characteristics influence the way people experience such interactions and comply with the persuasive attempts by robots. The purpose of this research is to identify how the (ostensible) gender and the facial characteristics of a robot influence the extent to which people trust it and the psychological reactance they experience from its persuasive attempts. This paper reports a laboratory study where SociBotTM, a robot capable of displaying different faces and dynamic social cues, delivered persuasive messages to participants while playing a game. In-game choice behavior was logged, and trust and reactance toward the advisor were measured using questionnaires. Results show that a robotic advisor with upturned eyebrows and lips (features that people tend to trust more in humans) is more persuasive, evokes more trust, and less psychological reactance compared to one displaying eyebrows pointing down and lips curled downwards at the edges (facial characteristics typically not trusted in humans). Gender of the robot did not affect trust, but participants experienced higher psychological reactance when interacting with a robot of the opposite gender. Remarkably, mediation analysis showed that liking of the robot fully mediates the influence of facial characteristics on trusting beliefs and psychological reactance. Also, psychological reactance was a strong and reliable predictor of trusting beliefs but not of trusting behavior. These results suggest robots that are intended to influence human behavior should be designed to have facial characteristics we trust in humans and could be personalized to have the same gender as the user. Furthermore, personalization and adaptation techniques designed to make people like the robot more may help ensure they will also trust the robot.
Keywords: Künstliche Intelligenz -
(2018): Poker Face Influence: Persuasive robot with minimal social cues triggers less psychological reactance. In: RO-MAN 2018 - 27th IEEE International Symposium on Robot and Human Interactive Communication, S. 940-946. DOI: 10.1109/ROMAN.2018.8525535
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(2018) : Toward an Organic Computing Approach to Automated Design of Processing Pipelines In: ARCS: ARCS Workshop 2018; 31th International Conference on Architecture of Computing Systems: Berlin, Germany: Axel Springer SE, S. 1-8. Online verfügbar unter https://ieeexplore.ieee.org/document/8385430/
Abstract: This paper aims to propose a novel Organic Computing concept to dealing with the overall issue of automated design of processing pipelines. It is outlined how several methods standing under the Artificial Intelligence umbrella are combined to form a technique that can be realized by Organic Computing systems to strengthen their self-configuration property by implementing self-optimization and self-learning techniques. Three envisioned application scenarios are discussed which will serve as first testbeds for the proposed architecture in a future research project: The automated design of 1) a data pre-processing observer component for the refurbishment and the analysis of insufficient quality data to improve the learning ability of employed machine learning algorithms, 2) an image processing pipeline for industrial imaging systems, and, 3) production lines in manufacturing scenarios.
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(2018): Virtual organizational design laboratory. Agent-based modeling of the co-evolution of social service delivery networks with population dynamics. In: Expert Systems with Applications 98, S. 189-204. DOI: 10.1016/j.eswa.2018.01.018
Abstract: This paper extends the concept of biological co-evolution to explain the performance and survival of one type of service organizations. It proposes that service delivery network design would benefit from complex adaptive systems (CAS) modeling approaches to recreate organizational phenomena driven by the interaction of the organization with its operating environment. This approach, paired with experimental design methods can serve as a virtual laboratory. We take the case of social service delivery (SSD) organizations that are structured as nonprofit organizations providing humanitarian assistance and relief services. These organizations often serve different geographical regions, thus, racial composition, migration patterns, and wealth of the populations served are factors that vary between locations. SSDs operate through service nodes (i.e., field offices, chapters, branches) in a network configuration. Therefore, the managerial decision of where to locate the field offices is an important one. An agent-based model to recreate agents' interactions as proxies of those exchanges occurring in real SSD settings is used. A series of validation experiments instill confidence that our model can be used as a virtual research laboratory. This paper contributes to the field of organizational design by testing a model able to recreate different policies that combined with different operating conditions impact the network over time and space. In addition, it provides experimental insights on what type of network configuration might provide a higher number of services delivered over time across the service network. The results can inform those defining the service system architecture looking to achieve SSD's goals considering the demographics of the markets served.
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(2018) : Readiness for Artificial Intelligence: 2018 IEEE 16th International Symposium on Intelligent Systems and Informatics (SISY): Subotica, Serbia: IEEE, S. 000235-000238
DOI: https://doi.org/10.1109/SISY.2018.8524740 Abstract: In the age of the fourth industrial revolution, where collaborative systems are the real innovation people are still not prepared for the results of the third revolution, namely for artificial intelligence. While every change has its life cycle with innovators, early adopters, early majority before it can reach its fruition, the age of robots has too fast been followed by the internet of things (IoT) of the fourth industrial revolution. Hence, people didn’t have enough time to adapt to the change. In present paper a primary research is presented, that aimed to explore the attitude of young adolescents towards artificial intelligence. Based on the result, trust is clearly one of the main issues regarding change in general and readiness in particular. People are not ready for robotic peers within their workplace yet. Psychological and emotive needs shall be addressed for the people to accept artificial intelligence in their workplace and surrounding.
Keywords: AI, Artificial intelligence, change, collaborative systems, early adopters, Employment, fourth industrial revolution, groupware, ieee xplore, innovation people, Internet of things, Künstliche Intelligenz, ORGANIZATIONS, PSYCHOLOGY, readiness, robotic peers, service robot, technological innovation, Trust -
(2018): Künstliche Intelligenz. Was sie kann & was uns erwartet. Originalausgabe. München: C.H. Beck
Abstract: Künstliche Intelligenz (KI) steht für Maschinen, die können, was der Mensch kann: hören und sehen, sprechen, lernen, Probleme lösen. In manchem sind sie inzwischen nicht nur schneller, sondern auch besser als der Mensch. Wie funktionieren diese klugen Maschinen? Bedrohen sie uns, machen sie uns gar überflüssig? Die Journalistin und KI-Expertin Manuela Lenzen erklärt anschaulich, was Künstliche Intelligenz kann und was uns erwartet. Künstliche Intelligenz ist das neue Zauberwort des digitalen Kapitalismus. Intelligente Computersysteme stellen medizinische Diagnosen und geben Rechtsberatung. Sie managen den Aktienhandel und steuern bald unsere Autos. Sie malen, dichten, dolmetschen und komponieren. Immer klügere Roboter stehen an den Fließbändern, begrüßen uns im Hotel, führen uns durchs Museum oder braten Burger und schnipseln den Salat dazu. Doch neben die Utopie einer schönen neuen intelligenten Technikwelt sind längst Schreckbilder getreten: von künstlichen Intelligenzen, die uns auf Schritt und Tritt überwachen, die unsere Arbeitsplätze übernehmen und sich unserer Kontrolle entziehen. Manuela Lenzen zeigt, welche Hoffnungen und Befürchtungen realistisch sind und welche in die Science Fiction gehören. Sie beschreibt, wie ein gutes Leben mit der Künstlichen Intelligenz aussehen könnte – und dass wir von klugen Maschinen eine Menge über uns selbst lernen können.
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(2018): The knowledge level in cognitive architectures. Current limitations and possible developments. In: COGNITIVE SYSTEMS RESEARCH 48, S. 39-55. DOI: 10.1016/j.cogsys.2017.05.001
Abstract: In this paper we identify and characterize an analysis of two problematic aspects affecting the representational level of cognitive architectures (CAs), namely: the limited size and the homogeneous typology of the encoded and processed knowledge. We argue that such aspects may constitute not only a technological problem that, in our opinion, should be addressed in order to build artificial agents able to exhibit intelligent behaviors in general scenarios, but also an epistemological one, since they limit the plausibility of the comparison of the CAs' knowledge representation and processing mechanisms with those executed by humans in their everyday activities. In the final part of the paper further directions of research will be explored, trying to address current limitations and future challenges. (C) 2017 Elsevier B.V. All rights reserved.
Keywords: Computerwissenschaft, Disziplin, Favoriten, formale Sprache, Formalisierung, Intellektualtechnik, Kogn. Architektur, Kognitionswissenschaft/Social Sciences/Humanities, kognitive Architekturen, Künstliche Intelligenz, Mensch-Technik-Relationen (MTR), natürliche Sprache, Ontologie, Philosophie, Realtechnik, Sprachverarbeitung, Technik, Technikphilosophie, Überblick, Weltwissen -
(2018): Social decisions and fairness change when people’s interests are represented by autonomous agents. In: Autonomous Agents and Multi-Agent Systems 32 (1), S. 163-187. DOI: 10.1007/s10458-017-9376-6
Abstract: There has been growing interest on agents that represent people's interests or act on their behalf such as automated negotiators, self-driving cars, or drones. Even though people will interact often with others via these agent representatives, little is known about whether people's behavior changes when acting through these agents, when compared to direct interaction with others. Here we show that people's decisions will change in important ways because of these agents; specifically, we showed that interacting via agents is likely to lead people to behave more fairly, when compared to direct interaction with others. We argue this occurs because programming an agent leads people to adopt a broader perspective, consider the other side's position, and rely on social norms-such as fairness-to guide their decision making. To support this argument, we present four experiments: in Experiment 1 we show that people made fairer offers in the ultimatum and impunity games when interacting via agent representatives, when compared to direct interaction; in Experiment 2, participants were less likely to accept unfair offers in these games when agent representatives were involved; in Experiment 3, we show that the act of thinking about the decisions ahead of time-i.e., under the so-called "strategy method"-can also lead to increased fairness, even when no agents are involved; and, finally, in Experiment 4 we show that participants were less likely to reach an agreement with unfair counterparts in a negotiation setting. We discuss theoretical implications for our understanding of the nature of people's social behavior with agent representatives, as well as practical implications for the design of agents that have the potential to increase fairness in society.
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(2018): Grundfragen der Maschinenethik. 2., durchgesehene Auflage. Ditzingen: Reclam
Abstract: Maschinen werden immer selbständiger, autonomer, intelligenter. Ihr Vormarsch ist kaum mehr zu stoppen. Dabei geraten sie in Situationen, die moralische Entscheidungen verlangen. Doch können Maschinen überhaupt moralisch handeln, sind sie moralische Akteure – und dürfen sie das? Mit diesen und ähnlichen Fragen beschäftigt sich der völlig neue Ansatz der Maschinenethik. Catrin Misselhorn erläutert die Grundlagen dieser neuen Disziplin an der Schnittstelle von Philosophie, Informatik und Robotik sachkundig und verständlich, etwa am Beispiel von autonomen Waffensystemen, Pflegerobotern und autonomem Fahren: das grundlegende Buch für die neue Disziplin.
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(2018) : A Rule-Based Approach for Self-Optimisation in Autonomic EHealth Systems In: ARCS: ARCS Workshop 2018; 31th International Conference on Architecture of Computing Systems: Berlin, Germany: Axel Springer SE, S. 1-4. Online verfügbar unter https://ieeexplore.ieee.org/document/8385432/
Abstract: Advances in machine learning techniques in recent years were of great benefit for the detection of diseases/medical conditions in eHealth systems, but only to a limited extend. In fact, while for the detection of some diseases the data mining techniques were performing very well, they still got outperformed by medical experts in about half of the tests done. In this paper, we propose a hybrid approach, which will use a rule-based system on top of the machine learning techniques in order to optimise the results of conflict handling. The goal is to insert the knowledge from medical experts in order to optimise the results given by the classification techniques. Possible positive and negative effects will be discussed.
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(2018): The essence of ethical reasoning in robot-emotion processing. In: International Journal of Social Robotics 10 (2), S. 211-223. DOI: 10.1007/s12369-017-0459-y
DOI: https://doi.org/10.1007/s12369-017-0459-y Abstract: As social robots become more and more intelligent and autonomous in operation, it is extremely important to ensure that such robots act in socially acceptable manner. More specifically, if such an autonomous robot is capable of generating and expressing emotions of its own, it should also have an ability to reason if it is ethical to exhibit a particular emotional state in response to a surrounding event. Most existing computational models of emotion for social robots have focused on achieving a certain level of believability of the emotions expressed. We argue that believability of a robot’s emotions, although crucially necessary, is not a sufficient quality to elicit socially acceptable emotions. Thus, we stress on the need of higher level of cognition in emotion processing mechanism which empowers social robots with an ability to decide if it is socially appropriate to express a particular emotion in a given context or it is better to inhibit such an experience. In this paper, we present the detailed mathematical explanation of the ethical reasoning mechanism in our computational model, EEGS, that helps a social robot to reach to the most socially acceptable emotional state when more than one emotions are elicited by an event. Experimental results show that ethical reasoning in EEGS helps in the generation of believable as well as socially acceptable emotions. (PsycINFO Database Record (c) 2019 APA, all rights reserved)
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(2018) : From social interaction to ethical AI: a developmental roadmap: 2018 Joint IEEE 8th International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob): Tokyo, Japan: IEEE, S. 204-211
DOI: https://doi.org/10.1109/DEVLRN.2018.8761023 Abstract: AI and robot ethics have recently gained a lot of attention because adaptive machines are increasingly involved in ethically sensitive scenarios and cause incidents of public outcry. Much of the debate has been focused on achieving highest moral standards in handling ethical dilemmas on which not even humans can agree, which indicates that the wrong questions are being asked. We suggest to address this ethics debate strictly through the lens of what behavior seems socially acceptable, rather than idealistically ethical. Learning such behavior puts the debate into the very heart of developmental robotics. This paper poses a roadmap of computational and experimental questions to address the development of socially acceptable machines. We emphasize the need for social reward mechanisms and learning architectures that integrate these while reaching beyond limitations of plain reinforcement-learning agents. We suggest to use the metaphor of “needs” to bridge rewards and higher level abstractions such as goals for both communication and action generation in a social context. We then suggest a series of experimental questions and possible platforms and paradigms to guide future research in the area.
Keywords: adaptive machines, Artificial intelligence, control engineering computing, Decision Making, developmental roadmap, developmental robotics, ethical AI, ethical aspects, ethically sensitive scenarios, Ethics, Face, ieee xplore, Künstliche Intelligenz, learning (artificial intelligence), plain reinforcement-learning agents, Robot Ethics, robot programming, Robot sensing systems, social interaction, social reward mechanisms, socially acceptable machines, standards -
(2018) : Emotionally Adaptive Driver Voice Alert System for Advanced Driver Assistance System (ADAS) Applications: 2018 International Conference on Smart Systems and Inventive Technology (ICSSIT): Tirunelveli, India: IEEE, S. 509-512
DOI: https://doi.org/10.1109/ICSSIT.2018.8748541 Abstract: Human cognitive analysis catalyzes the innovations in Human Machine Interface (HMI) for a variety of applications. In an Automotive Advanced Driver Assistance System (ADAS), the continuous cognitive interaction of the driver with the assistance system plays a crucial role in enhancing the active safety system. Multiple ADAS functionalities uses a variety of driver alerts through visual, audio and vibrational means to provide a numerous safety alerts to the driver. The effectiveness of any alert system is measured through its success rate in mitigating the actions which are against the alert commands. The actions taken by the driver for the alerts depends heavily on the driver’s moods, which are responsible for driver’s perception in understanding the alerts. Even though the voice alerts are considered as the most effective form of human alerts, the static nature of the voice alerts makes them less effective in making the driver to understand the criticality of the alerts when his moods are abnormal or having a reduced driving concentration levels. An adaptive voice alert system with a cognitive driver synchronization makes the alert penetration successful when the driver’s moods are abnormal or having a reduced driving concentration levels. Here in this paper the adaptive voice alert system is designed using the driver’s emotional cognitive features. The emotionally adaptive voice alert system changes the voice alerts as according to the moods of the driver, which are measured by Deep Learning based Emotion Recognition System. The adaptive voice alert system makes the voice enabled HMI effective which improves the vehicle safety.
Keywords: active safety system, adaptive driver voice alert system, Adaptive systems, Advanced Driver Assistance System (ADAS), advanced driver assistance system applications, Advanced driver assistance systems, alert commands, alert penetration successful, automotive advanced driver assistance system, Cognition, cognitive driver synchronization, Convolutional Neural Network (CNN), driver alerts, driver information systems, emotion recognition, emotion recognition system, Emotion Recognition System (ERS), emotionally adaptive voice alert system, human alerts, human cognitive analysis, Human Computer Interaction, Human Machine Interface (HMI), ieee xplore, Künstliche Intelligenz, learning (artificial intelligence), Monitoring, natural language interfaces, safety alerts, Vehicles, voice alerts -
(2018): Investigating People’s Rapport Building and Hindering Behaviors When Working with a Collaborative Robot. In: Int J of Soc Robotics 10 (1), S. 147-161. DOI: 10.1007/s12369-017-0441-8
DOI: https://doi.org/10.1007/s12369-017-0441-8 Abstract: Modern industrial robots are increasingly moving toward collaborating with people on complex tasks as team members, and away from working in isolated cages that are separated from people. Collaborative robots are programmed to use social communication techniques with people, enabling human team members to use their existing inter-personal skills to work with robots, such as speech, gestures, or gaze. Research is increasingly investigating how robots can use higher-level social structures such as team dynamics or conflict resolution. One particularly important aspect of human–human teamwork is rapport building: these are everyday social interactions between people that help to develop professional relationships by establishing trust, confidence, and collegiality, but which are formally peripheral to a task at hand. In this paper, we report on our investigations of how and if people apply similar rapport-building behaviors to robot collaborators. First, we synthesized existing human–human rapport knowledge into an initial human–robot interaction framework; this framework includes verbal and non-verbal behaviors, both for rapport building and rapport hindering, that people can be expected to exhibit. We developed a novel mock industrial task scenario that emphasizes ecological validity, and creates a range of social interactions necessary for investigating rapport. Finally, we report on a qualitative study that investigates how people use rapport hindering or building behaviors in our industrial scenario, which reflects how people may interact with robots in industrial settings.
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(2018): Attributions of Morality and Mind to Artificial Intelligence after Real-World Moral Violations. In: Computers in Human Behavior, S. 401-411. DOI: 10.1016/j.chb.2018.05.014
Abstract: Highlights: •AI’s real-world moral violations only reported 43.5% of the time •Knowledge of algorithm increases moral violation perception •AI not organization, programmer, or use is attributed wrongness •AI attributed mind, awareness, intentionality, justification, and responsibility