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Design Principles for Creating Human-Shapable Agents.
W. Bradley Knox,
Ian Fasel, and Peter
Stone.
In AAAI Spring 2009 Symposium on Agents that Learn from Human Teachers, March 2009.
AAAI
Spring 2009 Symposium: Agents that Learn from Human Teachers
[PDF]400.8kB [postscript]2.1MB
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\footnoteIn this paper, we distinguish between human reinforcement and environmental reward within an MDP. To avoid confusion, human feedback is always called ``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 \em 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.
@InProceedings{AAAIsymp09-knox,
author="W.\ Bradley Knox and Ian Fasel and Peter Stone",
title="Design Principles for Creating Human-Shapable Agents",
booktitle="AAAI Spring 2009 Symposium on Agents that Learn from Human Teachers",
month="March",
year="2009",
abstract={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\footnote{In this
paper, we distinguish between human reinforcement and environmental
reward within an MDP. To avoid confusion, human feedback is always
called ``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 {\em
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.},
wwwnote={<a href="http://www.aaai.org/Symposia/Spring/sss09.php">AAAI Spring 2009 Symposium: Agents that Learn from Human Teachers</a>},
}
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