Alle Publikationen
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2016
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Absher, John R.; Cloutier, Jasmin (Hg.) (2016): Neuroimaging personality, social cognition, and character. Amsterdam, Netherlands: Elsevier
DOI: https://doi.org/10.1016/B978-0-12-800935-2.00012-9 Abstract: Learning new, and regulating existing, emotional responses are two intimately connected survival functions that enable us to flexibly adapt in a constantly changing world. Basic and clinical research has long documented the behavioral effects of these functions in humans and other animals. Here, we survey recent developments in the research on the transmission of emotionally relevant information. In particular, we discuss experiments using behavioral and neurobiological methods to examine how fear and safety information is transmitted between individuals through observation. We discuss how these findings can be understood in light of studies of empathy and mental state attributions and present a simple neurobiological model of emotional learning and regulation that is compatible with empirical evidence in psychology and neuroscience. We close by outlining a few outstanding challenges for future research on social learning and regulation.
2015
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(2015): Reinforcement-learning based dialogue system for human–robot interactions with socially-inspired rewards. In: Computer Speech & Language 34 (1), S. 256-274. DOI: 10.1016/j.csl.2015.03.007
DOI: https://doi.org/10.1016/j.csl.2015.03.007 Abstract: This paper investigates some conditions under which polarized user appraisals gathered throughout the course of a vocal interaction between a machine and a human can be integrated in a reinforcement learning-based dialogue manager. More specifically, we discuss how this information can be cast into socially-inspired rewards for speeding up the policy optimisation for both efficient task completion and user adaptation in an online learning setting. For this purpose a potential-based reward shaping method is combined with a sample efficient reinforcement learning algorithm to offer a principled framework to cope with these potentially noisy interim rewards. The proposed scheme will greatly facilitate the system's development by allowing the designer to teach his system through explicit positive/negative feedbacks given as hints about task progress, in the early stage of training. At a later stage, the approach will be used as a way to ease the adaptation of the dialogue policy to specific user profiles. Experiments carried out using a state-of-the-art goal-oriented dialogue management framework, the Hidden Information State (HIS), support our claims in two configurations: firstly, with a user simulator in the tourist information domain (and thus simulated appraisals), and secondly, in the context of man–robot dialogue with real user trials.
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 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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