Alle Publikationen
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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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