Neuro-Dynamic Programming
Three sources of information in social learning
Imitation in animals and artifacts
Social learning mechanisms compared in a simple environment
ICAL 2003 Proceedings of the eighth international conference on Artificial life
Bayesian inverse reinforcement learning
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Active Learning for Reward Estimation in Inverse Reinforcement Learning
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
Exploiting social partners in robot learning
Autonomous Robots
Active learning of inverse models with intrinsically motivated goal exploration in robots
Robotics and Autonomous Systems
Socially guided intrinsic motivation for robot learning of motor skills
Autonomous Robots
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In this article we propose a computational model that describes how observed behavior can influence an observer's own behavior, including the acquisition of new task descriptions. The sources of influence on our model's behavior are: beliefs about the world's possible states and actions causing transitions between them; baseline preferences for certain actions; a variable tendency to infer and share goals in observed behavior; and a variable tendency to act efficiently to reach rewarding states. Acting on these premises, our model is able to replicate key empirical studies of social learning in children and chimpanzees. We demonstrate how a simple artificial system can account for a variety of biological social transfer phenomena, such as goal-inference and over-imitation, by taking into account action constraints and incomplete knowledge about the world dynamics.