Technical Note: \cal Q-Learning
Machine Learning
The role of emotion in believable agents
Communications of the ACM
Virtual petz (video session): a hybrid approach to creating autonomous, lifelike dogz and catz
AGENTS '98 Proceedings of the second international conference on Autonomous agents
A social reinforcement learning agent
Proceedings of the fifth international conference on Autonomous agents
Integrated learning for interactive synthetic characters
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
ACM SIGGRAPH Computer Graphics
Old tricks, new dogs: ethology and interactive creatures
Old tricks, new dogs: ethology and interactive creatures
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Ontology-based examinational students work retrieval
CompSysTech '08 Proceedings of the 9th International Conference on Computer Systems and Technologies and Workshop for PhD Students in Computing
Emotion and reinforcement: affective facial expressions facilitate robot learning
ICMI'06/IJCAI'07 Proceedings of the ICMI 2006 and IJCAI 2007 international conference on Artifical intelligence for human computing
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Teachable characters can enhance entertainment technology by providing new interactions, becoming more competent at game play, and simply being fun to teach. It is important to understand how human players try to teach virtual agents in order to design agents that learn effectively from this instruction. We present results of a user study where people teach a virtual agent a novel task within a reinforcement-based learning framework. Analysis yields lessons of how human players approach the task of teaching a virtual agent: 1) they want to direct the agent’s attention; 2) they communicate both instrumental and motivational intentions; 3) they tailor their instruction to their understanding of the agent; and 4) they use negative communication as both feedback and as a suggestion for the next action. Based on these findings we modify the agent’s learning algorithm and show improvements to the learning interaction in follow-up studies. This work informs the design of real-time learning agents that better match human teaching behavior to learn more effectively and be more enjoyable to teach.