Mental models: towards a cognitive science of language, inference, and consciousness
Mental models: towards a cognitive science of language, inference, and consciousness
The media equation: how people treat computers, television, and new media like real people and places
A social reinforcement learning agent
Proceedings of the fifth international conference on Autonomous agents
Designing Sociable Robots
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Recognition of Affective Communicative Intent in Robot-Directed Speech
Autonomous Robots
Humanoid Robots: A New Kind of Tool
IEEE Intelligent Systems
Propagation of Q-values in Tabular TD(lambda)
ECML '02 Proceedings of the 13th European Conference on Machine Learning
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Temporal credit assignment in reinforcement learning
Temporal credit assignment in reinforcement learning
Socially guided machine learning
Socially guided machine learning
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
The cog project: building a humanoid robot
Computation for metaphors, analogy, and agents
A neural network model for classification of facial expressions based on dimension model
ICCS'05 Proceedings of the 5th international conference on Computational Science - Volume Part III
Effects of Polite Behaviors Expressed by Robots: A Case Study in Japan
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 02
Effects of Polite Behaviors Expressed by Robots: A Psychological Experiment in Japan
International Journal of Synthetic Emotions
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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.