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2018
Cruz–Maya, A.; Tapus, A. (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
Sarala, S.; Yadav, D. S.; Ansari, A. (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
2008
Lim, H. C.; Stocker, R.; Larkin, H. (2008) : Ethical Trust and Social Moral Norms Simulation: A Bio-inspired Agent-Based Modelling Approach: 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, 2: Washington, DC, US: IEEE Computer Society, S. 245-251

DOI: https://doi.org/10.1109/WIIAT.2008.184

Abstract: The understanding of the micro-macro link is an urgent need in the study of social systems. The complex adaptive nature of social systems adds to the challenges of understanding social interactions and system feedback and presents substantial scope and potential for extending the frontiers of computer-based research tools such as simulations and agent-based technologies. In this project, we seek to understand key research questions concerning the interplay of ethical trust at the individual level and the development of collective social moral norms as representative sample of the bigger micro-macro link of social systems. We outline our computational model of ethical trust (CMET) informed by research findings from trust, machine ethics and neural science. Guided by the CMET architecture, we discuss key implementation ideas for the simulations of ethical trust and social moral norms.

Keywords: Adaptive systems, Agent Ensemble, Analytical models, Australia, bio-inspired agent, complex adaptive nature, computational model, Computational modeling, Computer Simulation, computer-based research tools, Context modeling, ethical aspects, ethical trust, Ethics, ieee xplore, intelligent agent, Intelligent structures, micromacro link, Moral & Ethik, moral norms, Object oriented modeling, research questions, Social interactions, social moral norms simulation, social sciences

2018

2008

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