Alle Publikationen
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2019
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(2019): On Proactive, Transparent, and Verifiable Ethical Reasoning for Robots. In: Proceedings of the IEEE 107 (3), S. 541-561. DOI: 10.1109/JPROC.2019.2898267
DOI: https://doi.org/10.1109/JPROC.2019.2898267 Abstract: Previous work on ethical machine reasoning has largely been theoretical, and where such systems have been implemented, it has, in general, been only initial proofs of principle. Here, we address the question of desirable attributes for such systems to improve their real world utility, and how controllers with these attributes might be implemented. We propose that ethically critical machine reasoning should be proactive, transparent, and verifiable. We describe an architecture where the ethical reasoning is handled by a separate layer, augmenting a typical layered control architecture, ethically moderating the robot actions. It makes use of a simulation-based internal model and supports proactive, transparent, and verifiable ethical reasoning. To do so, the reasoning component of the ethical layer uses our Python-based belief-desire-intention (BDI) implementation. The declarative logic structure of BDI facilitates both transparency, through logging of the reasoning cycle, and formal verification methods. To prove the principles of our approach, we use a case study implementation to experimentally demonstrate its operation. Importantly, it is the first such robot controller where the ethical machine reasoning has been formally verified.
Keywords: BDI implementation, belief desire intention implementation, control engineering computing, Design methodology, ethical machine reasoning, ethical reasoning, Ethics, formal verification, ieee xplore, intelligent robots, layered control architecture, learning (artificial intelligence), machine learning, Moral & Ethik, Predictive models, Python, robot controller, robot programming, Robots, safety, simulation-based internal model, Social implications of technology, software architecture, transparency -
(2019) : Good Robot Design or Machiavellian? An In-the-Wild Robot Leveraging Minimal Knowledge of Passersby’s Culture: 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI): Daegu, Korea: IEEE, S. 382-391
DOI: https://doi.org/10.1109/HRI.2019.8673326 Abstract: Social robots are being designed to use human-like communication techniques, including body language, social signals, and empathy, to work effectively with people. Just as between people, some robots learn about people and adapt to them. In this paper we present one such robot design: we developed Sam, a robot that learns minimal information about a person’s background, and adapts to this background. Our in-the-wild study found that people helped Sam for significantly longer when it adapted to match their background. While initially we saw this as a success, in re-considering our study we started seeing a different angle. Our robot effectively deceived people (changed its story and text), based on some knowledge of their background, to get more work from them. There was little direct benefit to the person from this adaptation, yet the robot stood to gain free labor. We would like to pose the question to the community: is this simply good robot design, or, is our robot being manipulative? Where does the ethical line lay between a robot leveraging social techniques to improve interaction, and the more negative framing of a robot or algorithm taking advantage of people? How can we decide what is good here, and what is less desirable?
Keywords: Body language, Cultural differences, Culture, Ethics, Global communication, human-like communication techniques, human-robot interaction, ieee xplore, in the wild, in-the-wild robot, learning (artificial intelligence), minimal information, Mobile robots, Mood, Moral & Ethik, passersby culture, Persuasive Robots, robot design, Robots, Sam, Shape, social robots, Social signals, social techniques, Task Analysis 2018
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(2018) : Adapting Robot Behavior using Regulatory Focus Theory, User Physiological State and Task-Performance Information: 2018 27th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN): Nanjing, China: IEEE Robotics & Automation Society, S. 644-651
DOI: https://doi.org/10.1109/ROMAN.2018.8525648 Abstract: Social robots are expected to be part of everyday life of people. This will generate interactions between humans and robots that may have positive or negative effects on the users. In order to minimize the negative effects and increase robot persuasiveness, robots should behave in an appropriate manner by adapting to their users. How to achieve this adaptation remains a challenge. We propose the usage of the Regulatory Focus Theory, user physiological state, and game-performance information in order to detect user stress and adapt the behavior of the robot. We present a longitudinal experiment conducted with 35 participants in a game-like scenario. The robot was trained for adapting to the regulatory focus of the users and decreasing their stress while they were playing the game. For this reason, we trained the robot with 12 participants with Chronic Promotion State and with 12 participants with Chronic Prevention State. We used a Q-Learning algorithm based on the Regulatory Focus of the participants, user stress, and task performance. The model obtained was tested with 2 groups (6 and 5 participants, respectively) according to their Chronic Regulatory Focus. Results show that our system was able to generate a robot behavior capable of increasing robot persuasiveness and reducing user stress, which is of great importance for social robots.
Keywords: Adaptive systems, Angemessen(heit) (von Technik), chronic promotion state, chronic regulatory focus, game-like scenario, game-performance information, Games, human-robot interaction, ieee xplore, learning (artificial intelligence), physiology, regulatory focus theory, robot behavior, robot persuasiveness, Robot sensing systems, social robots, Stress, Task Analysis, Task Performance, task-performance information, user physiological state -
(2018) : ETHICAL FRAMEWORK FOR MACHINE LEARNING: 2018 ITU Kaleidoscope: Machine Learning for a 5G Future (ITU K): Santa Fe, Argentina: IEEE, S. 1-8
DOI: https://doi.org/10.23919/ITU-WT.2018.8597767 Abstract: Artificial Intelligence (AI) with its core subset of Machine Learning (ML) is rapidly transforming life experiences as humans begin to grow more dependent on these ‘smart machines’ for their needs - ranging from routine mundane chores to critical personal decisions. However, these transformative technologies are at the same time proving unpredictable too as has been reported worldwide in certain cases. Therefore, several studies/reports, such as COMEST report on Robotics ethics (UNESCO, 2017) point to an obvious need for inculcating more ethical behavior in machines. The present study aims to look at the role and interplay of ML (the hard sciences) and Ethics (the soft sciences) to resolve such predicaments that are inadvertently manifested by machines not constrained or controlled by human expectations. Based on focused review of literature of both domains-ML and Ethics, the proposed paper attempts to first build on the need for introduction of an ethical algorithm in the domain of machine learning and then endeavors to provide a conceptual framework to resolve the ethical dilemmas.
Keywords: Artificial intelligence, Artificial intelligence/machine learning, Big Data, COMEST report, critical personal decisions, Decision Making, design approach, domains-ML, emotional quotient, ethical algorithm, ethical aspects, ethical behavior, ethical dilemmas, ethical framework, Ethics, hard sciences, human expectations, ieee xplore, learning (artificial intelligence), machine learning, Machine learning algorithms, Moral & Ethik, Prediction algorithms, robotic ethics, Robots, routine mundane chores, smart machines, soft sciences, spiritual quotient, transformative technologies -
(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 2017
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(2017) : Fourth Industrial Revolution and the future of Engineering: Could Robots Replace Human Jobs? How Ethical Recommendations can Help Engineers Rule on Artificial Intelligence: 2017 7th World Engineering Education Forum (WEEF): Kuala Lumpur, Malaysia: IEEE, S. 21-26
DOI: https://doi.org/10.1109/WEEF.2017.8466973 Abstract: The current economic crisis, combined with the sudden increased use of Information Technology (IT) and Artificial Intelligence (AI) in human life presents new challenges and opportunities for engineering education and jobs. Engineering education needs to undergo a revolution; ethical issues that involve AI and the use of technologies need to be inserted in the learning process urgently to guarantee the employment of our future engineers. In this work we identified and evaluated issues and candidate recommendations from initiatives like “The IEEE Global Initiative for Ethical Considerations in Artificial Intelligence and Autonomous Systems” and “USA NSTC’s Subcommittee on Machine Learning and Artificial Intelligence” in order to provide a set of ethical principles and recommendations that are conversant with Engineering ethics defined by “Engineering Criteria 2000”. Some of these ethical principles and recommendations are highlighted with the objective of introducing AI in engineering education and improve the connections between technology and society.
Keywords: AI, Artificial intelligence, candidate recommendations, Companies, control engineering education, economic crisis, Engineering Criteria 2000, engineering education, Engineering ethics, ethical aspects, ethical considerations, ethical issues, ethical principles, ethical recommendations, fourth industrial revolution, human life, ieee xplore, Industries, Industry 4.0, Information technology, intelligent robots, Labor Market, Law, learning (artificial intelligence), Moral & Ethik, service robot -
(2017) : Turn-taking intention recognition using multimodal cues in social human-robot interaction: 2017 17th International Conference on Control, Automation and Systems (ICCAS): Jeju, Korea: IEEE, S. 1300-1302
DOI: https://doi.org/10.23919/ICCAS.2017.8204407 Abstract: Turn-taking is an essential social skill for human communication. The robot needs to recognize the end of turn for timely response to the user with little delay in human-robot interaction. In this paper, we propose a turn-taking intention recognition system that determine the timing of turn-taking using multimodal cues in social Human-Robot Interaction (sHRI). In order to evaluate the turn-taking intention recognition system, we collect multimodal data set and conducted experiments. To that end, we designed a human-robot interaction scenario including turn-taking and conducted an experiment with 30 participants using the humanoid robot NAO. In experiments, we validate recognition models trained multimodal dataset by machine learning methods.
Keywords: Encoding, essential social skill, Floors, human communication, humanoid robot NAO, Humanoid Robots, human-robot interaction, ieee xplore, Künstliche Intelligenz, learning (artificial intelligence), Lips, machine learning methods, multimodal cues, multimodal dataset, Robots, sHRI, social human-robot interaction, Social robot, speech, timing, Turn-Taking, turn-taking intention recognition system -
(2017) : Learning behavioral norms in uncertain and changing contexts: 2017 8th IEEE International Conference on Cognitive Infocommunications (CogInfoCom): Debrecen, Hungary: IEEE, S. 000301-000306
DOI: https://doi.org/10.1109/CogInfoCom.2017.8268261 Abstract: Human behavior is often guided by social and moral norms. Robots that enter human societies must therefore behave in norm-conforming ways as well to increase coordination, predictability, and safety in human-robot interactions. However, human norms are context-specific and laced with uncertainty, making the representation, learning, and communication of norms challenging. We provide a formal representation of norms using deontic logic, Dempster-Shafer Theory, and a machine learning algorithm that allows an artificial agent to learn norms under uncertainty from human data. We demonstrate a novel cognitive capability with which an agent can dynamically learn norms while being exposed to distinct contexts, recognizing the unique identity of each context and the norms that apply in it.
Keywords: Artificial agent, behavioral norms, behavioural sciences computing, Cognition, Conferences, Dempster-Shafer Theory, deontic logic, Ethics, formal logic, Human behavior, human societies, human-robot interaction, human-robot interactions, ieee xplore, inference mechanisms, learning (artificial intelligence), Libraries, machine learning algorithm, Moral & Ethik, moral norms, norm-conforming ways, Robot kinematics, Social Norms, uncertainty, uncertainty handling -
(2017) : Acquiring social interaction behaviours for telepresence robots via deep learning from demonstration: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS): Vancouver, British Columbia, Canada: IEEE, S. 37-42
DOI: https://doi.org/10.1109/IROS.2017.8202135 Abstract: As robots begin to inhabit public and social spaces, it is increasingly important to ensure that they behave in a socially appropriate way. However, manually coding social behaviours is prohibitively difficult since social norms are hard to quantify. Therefore, learning from demonstration (LfD), wherein control policies are inferred from demonstrations of correct behaviour, is a powerful tool for helping robots acquire social intelligence. In this paper, we propose a deep learning approach to learning social behaviours from demonstration. We apply this method to two challenging social tasks for a semi-autonomous telepresence robot. Our results show that our approach outperforms gradient boosting regression and performs well against a hard-coded controller. Furthermore, ablation experiments confirm that each element of our method is essential to its success.
Keywords: ablation experiments, challenging social tasks, Cloning, control engineering computing, correct behaviour, deep learning approach, deep learning from demonstration, gradient boosting regression, gradient methods, hard-coded controller, human-robot interaction, ieee xplore, Künstliche Intelligenz, learning (artificial intelligence), LfD, machine learning, public spaces, Regression Analysis, Robot sensing systems, semiautonomous telepresence robot, social behaviour, Social intelligence, social interaction behaviours, Social Norms, social spaces, telepresence robots 2015
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(2015) : May I help you? - Design of Human-like Polite Approaching Behavior-: 2015 10th ACM/IEEE International Conference on Human-Robot Interaction (HRI): Portland, Oregon, USA: Association for Computing Machinery, S. 35-42
Abstract: When should service staff initiate interaction with a visitor? Neither simply-proactive (e.g. talk to everyone in a sight) nor passive (e.g. wait until being talked to) strategies are desired. This paper reports our modeling of polite approaching behavior. In a shopping mall, there are service staff members who politely approach visitors who need help. Our analysis revealed that staff members are sensitive to ‘intentions’ of nearby visitors. That is, when a visitor intends to talk to a staff member and starts to approach, the staff member also walks a few steps toward the visitors in advance to being talked. Further, even when not being approached, staff members exhibit ”availability” behavior in the case that a visitor’s intention seems uncertain. We modeled these behaviors that are adaptive to pedestrians’ intentions, occurred prior to initiation of conversation. The model was implemented into a robot and tested in a real shopping mall. The experiment confirmed that the proposed method is less intrusive to pedestrians, and that our robot successfully initiated interaction with pedestrians.
Keywords: Adaptation models, availability behavior, Behavior Design, Collaboration, Estimation, ieee xplore, initiation of interaction, Intention estimation, Künstliche Intelligenz, learning (artificial intelligence), Micromechanical devices, nearby visitors, polite approaching behavior, robot programming, Robot sensing systems, service staff members, shopping mall, Task Analysis 2014
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(2014) : Personalizing robot behavior for interruption in social human-robot interaction: 2014 IEEE International Workshop on Advanced Robotics and its Social Impacts: Evanston, IL: IEEE, S. 44-49
DOI: https://doi.org/10.1109/ARSO.2014.7020978 Abstract: People engaging in an activity usually has individual tolerance to be interrupted [1], [2]. Humans subconsciously adapt their behaviors to draw other one’s attention and to get into a conversation based on their historical experiences, but robots often fail to be aware of humans’ feeling and thus interrupt their users repeatedly. To endow service robots with such socially acceptable ability, we propose an online human-aware interactive learning framework in this paper, under which the robot personalizes its behaviors according to both observed user’s attention and its conjecture about user’s awareness of itself. To this purpose, the correlation between the robot’s theory of awareness, user’s attention and robot behavior are explored through reinforcement learning techniques. The conducted experiment shows that the robot can personalize its interruption strategy, and the optimal policies converged for at least 26 episodes.
Keywords: Face, Hidden Markov models, human-robot interaction, ieee xplore, Interrupters, interruption strategy, Künstliche Intelligenz, learning (artificial intelligence), Markov processes, online human-aware interactive learning framework, reinforcement learning techniques, robot behavior personalization, Robot sensing systems, robot theory of awareness, service robot, social human-robot interaction, social sciences, user attention, user awareness 2013
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(2013) : Personal service: A robot that greets people individually based on observed behavior patterns: 2013 8th ACM/IEEE International Conference on Human-Robot Interaction (HRI): Tokyo, Japan: IEEE, S. 129-130
DOI: https://doi.org/10.1109/HRI.2013.6483535 Abstract: We are developing an interactive service robot which provides personal greetings to customers, using a machine-learning approach based on observations of a customer’s appearance or behavior from on-board or environmental sensors. For each visit, several features are recorded, such as “time of day” or “number of people in group.” A set of classifiers trained by human coders compare the current features with the person’s individual history, to determine an appropriate feature for a robot to speak about. This system enables the robot to make context-appropriate comments such as “good morning, you’re here very early today.” We present the design of our system and an encouraging set of preliminary prediction results based on one month of data taken from real customers at a shopping mall.
Keywords: Accuracy, Angemessen(heit) (von Technik), context-appropriate comments, customer appearance, customer behavior, environmental sensors, Feature extraction, History, human coders, human-robot interaction, ieee xplore, interactive service robot, learning (artificial intelligence), long-term interaction, machine-learning approach, observed behavior patterns, personal greetings, personal service, Robot sensing systems, Sensors, service robot, shopping mall 2010
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(2010) : Effect of social robot’s behavior in collaborative learning: 2010 5th ACM/IEEE International Conference on Human-Robot Interaction (HRI): Osaka, Japan: IEEE, S. 195-196
DOI: https://doi.org/10.1109/HRI.2010.5453199 Abstract: This paper describes about the effect of social robot’s behavior on human performance. The robot behaves based on an artificial mind model, and it expresses emotions according to the situation. In this research, we consider about the case where human and the robot learn cooperatively. The robot emotionally reacts to the joint learner’s success and failure. The experimental result shows that social behavior of the robot influences the performance of human learners.
Keywords: Animals, Anthropomorphism, APPRAISAL, Artificial intelligence, artificial mind model, collaborative learning, Collaborative work, Computer science education, Engines, groupware, human learners, human performance, Human robot interaction, human-robot interaction, ieee xplore, Künstliche Intelligenz, learning (artificial intelligence), Mobile robots, Personality, robot emotion, Social robot, social robot behavior, Switches 2009
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(2009) : Robots with emotional intelligence: 2009 4th ACM/IEEE International Conference on Human-Robot Interaction (HRI): 2009 4th ACM/IEEE International Conference on Human-Robot Interaction (HRI): New York, NY, US: Association for Computing Machinery
DOI: https://doi.org/10.1145/1514095.1514098 Abstract: This keynote talk will illustrate a basic set of skills of emotional intelligence, how they are important for robots and agents that interact with people, and how our research at MIT addresses part of the problem of giving robots such skills. One of the most important skills is the ability to perceive and understand expressions of emotion, which I will highlight by demonstrating new technologies developed to read joint facial-head movements in real-time and associate these with complex affective-cognitive states, and technologies to read paralinguistic vocal cues from speech. I will also show some non-traditional ways robots might sense and learn about human emotion, and ways they can respond to what they sense that can help or hurt people. I will discuss social and ethical issues these technologies raise. Finally, I will present some new possibilities for robots to both learn from people and help teach skills of emotional intelligence to people, especially to those with nonverbal learning impairments who often want to learn these skills, including many people with diagnoses of autism spectrum disorders such as Aspergers Syndrome.
Keywords: Affective computing, affective-cognitive state, Artificial intelligence, Aspergers Syndrome, Autism, Autism spectrum disorder, behavioural sciences, computer aided instruction, deception detection, Educational robots, emotion expression, emotion perception, emotion recognition, emotion understanding, Emotional Intelligence, empathic technology, ethical aspects, ethical issue, facial expression recognition, facial-head movement, human emotion learning, human emotion sensing, human-robot interaction, ieee xplore, image motion analysis, intelligent robots, Laboratories, learning (artificial intelligence), Media, medical disorders, MIT, Moral & Ethik, nonverbal learning impairment, paralinguistic vocal cue, physiological sensing, prosody analysis, robot emotional intelligence, Robot sensing systems, skills teaching, social issue, speech processing, system, Teaching 2008
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(2008) : Learning polite behavior with situation models: 3rd ACM/IEEE International Conference on Human-Robot Interaction (HRI): New York, NY, US: Association for Computing Machinery, S. 209-216
DOI: https://doi.org/10.1145/1349822.1349850 Abstract: In this paper, we describe experiments with methods for learning the appropriateness of behaviors based on a model of the current social situation. We first review different approaches for social robotics, and present a new approach based on situation modeling. We then review algorithms for social learning and propose three modifications to the classical Q-Learning algorithm. We describe five experiments with progressively complex algorithms for learning the appropriateness of behaviors. The first three experiments illustrate how social factors can be used to improve learning by controlling learning rate. In the fourth experiment we demonstrate that proper credit assignment improves the effectiveness of reinforcement learning for social interaction. In our fifth experiment we show that analogy can be used to accelerate learning rates in contexts composed of many situations.
Keywords: Angemessen(heit) (von Technik), Convergence, credit assignment, Humans, ieee xplore, Learning, learning (artificial intelligence), Learning by Analogy, machine learning, polite behavior, Q-Learning, Q-learning algorithm, Reinforcement learning, Robot sensing systems, Robots, situation modeling, social aspects of automation, Social factors, social interaction, social learning, Social robotic, social situation, standards -
(2008) : IslEnquirer: Social user model acquisition through network analysis and interactive learning: 2008 IEEE Spoken Language Technology Workshop: Goa, India: IEEE, S. 117-120
DOI: https://doi.org/10.1109/SLT.2008.4777854 Abstract: We present an approach to introduce social awareness in interactive systems. The IslEnquirer is a system which automatically builds social user models. It initializes the models by social network analysis of available offline data. These models are then verified and extended by interactive learning which is carried out by a robot initiated spoken dialog with the user.
Keywords: Automatic speech recognition, Automation, Cognitive robotics, Context modeling, Data Mining, Humanoid Robots, Humans, ieee xplore, Information retrieval, interactive learning, interactive system, interactive systems, IslEnquirer, Künstliche Intelligenz, learning (artificial intelligence), Robot, Robotics, social awareness, social network analysis, Social network services, social networking (online), social networks, social user model acquisition, spoken dialog, spoken dialog system, user modeling, user modelling 2007
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(2007) : Towards Realistic Facial Behaviour in Humanoids - Mapping from Video Footage to a Robot Head: 2007 IEEE 10th International Conference on Rehabilitation Robotics: Piscataway NJ: IEEE, S. 833-840
DOI: https://doi.org/10.1109/ICORR.2007.4428521 Abstract: Rehabilitation robotics and physical therapy could greatly benefit from engaging and motivating, robotic caregivers which respond in accordance to patients’ emotional and social cues. Recent studies indicate that human-machine interactions are more believable and memorable when a physical entity is present, provided that the machine behaves in a realistic manner. It is desirable to adopt face-to-face communication because it is the most natural and efficient way of exchanging information and does not require users to alter their habits. Towards this end, we describe a process for animating a robot head, based on video input of a human head. We map from the 2D coordinates of feature points into the robot’s servo space using Partial Least Squares (PLS). Learning is done using a small set of keyframes manually created by an animator. The method is efficient, robust to tracking errors and independent of the scale of the face being tracked.
Keywords: Animation, Computer animation, Facial animation, human-machine interactions, Humanoid Robots, Humanoids, Humans, ieee xplore, Künstliche Intelligenz, Learning, learning (artificial intelligence), Magnetic heads, Man machine systems, medical robotics, Medical treatment, Orbital robotics, partial least squares, patient rehabilitation, physical therapy, realistic facial behaviour, Rehabilitation robotics, robot head, Robot kinematics, robot servo space, servomechanisms, video footage 2006
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(2006) : Learning polite behavior with situation models: 3rd International Forum on Applied Wearable Computing 2006: Bremen, Germany: IEEE, S. 209-216
DOI: https://doi.org/10.1145/1349822.1349850 Abstract: In this paper, we describe experiments with methods for learning the appropriateness of behaviors based on a model of the current social situation. We first review different approaches for social robotics, and present a new approach based on situation modeling. We then review algorithms for social learning and propose three modifications to the classical Q-Learning algorithm. We describe five experiments with progressively complex algorithms for learning the appropriateness of behaviors. The first three experiments illustrate how social factors can be used to improve learning by controlling learning rate. In the fourth experiment we demonstrate that proper credit assignment improves the effectiveness of reinforcement learning for social interaction. In our fifth experiment we show that analogy can be used to accelerate learning rates in contexts composed of many situations.
Keywords: Convergence, credit assignment, Humans, ieee xplore, Künstliche Intelligenz, Learning, learning (artificial intelligence), Learning by Analogy, machine learning, polite behavior, Q-Learning, Q-learning algorithm, Reinforcement learning, Robot sensing systems, Robots, situation modeling, social aspects of automation, Social factors, social interaction, social learning, Social robotic, social situation, standards
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