In order for learning agents to be useful to non-technical users, it is important to be able to teach agents how to perform new tasks using simple communication methods. We begin this paper by describing a framework we recently developed called Training an Agent Manually via Evaluative Reinforcement (TAMER), which allows a human to train a learning agent by giving simple scalar reinforcement signals while observing the agent perform the task. We then discuss how this work fits into a general taxonomy of methods for human-teachable (HT) agents and argue that the entire field of HT agents could benefit from an increased focus on the human side of teaching interactions. We then propose a set of conjectures about aspects of human teaching behavior that we believe could be incorporated into future work on HT agents.

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In AAAI Spring 2009 Symposium on Agents that Learn from Human Teachers, March 2009.

@InProceedings{AAAIsymp09-knox, title={Design Principles for Creating Human-Shapable Agents}, author={W. Bradley Knox and Ian Fasel and Peter Stone}, booktitle={AAAI Spring 2009 Symposium on Agents that Learn from Human Teachers}, month={March}, url="http://www.cs.utexas.edu/users/ai-lab?knox:aaaisymp09", year={2009} }