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Lisa Torrey and Matthew E. Taylor. Help an Agent Out: Student/Teacher Learning
in Sequential Decision Tasks. In Proceedings of the Adaptive and Learning Agents workshop (at AAMAS-12), June 2012.
ALA-12
Research on agents has led to the development of algorithms for learning from experience, accepting guidance from humans, and imitating experts. This paper explores a new direction for agents: the ability to teach other agents. In particular, we focus on situations where the teacher has limited expertise and instructs the student through action advice. The paper proposes and evaluates several teaching algorithms based on providing advice at a gradually decreasing rate. A crucial component of these algorithms is the ability of an agent to estimate its confidence in a state. We also contribute a student/teacher framework for implementing teaching strategies, which we hope will spur additional development in this relatively unexplored area.
@inproceedings(ALA12-Torrey,
author="Lisa Torrey and Matthew E. Taylor",
title="Help an Agent Out: Student/Teacher Learning in Sequential Decision Tasks",
Booktitle="Proceedings of the Adaptive and Learning Agents workshop (at AAMAS-12)",
month="June",
year= "2012",
wwwnote={<a href="http://como.vub.ac.be/ALA2012/">ALA-12</a>},
abstract="Research on agents has led to the development of algorithms for
learning from experience, accepting guidance from humans, and
imitating experts. This paper explores a new direction for agents:
the ability to teach other agents. In particular, we focus on situations
where the teacher has limited expertise and instructs the
student through action advice. The paper proposes and evaluates
several teaching algorithms based on providing advice at a gradually
decreasing rate. A crucial component of these algorithms is
the ability of an agent to estimate its confidence in a state. We also
contribute a student/teacher framework for implementing teaching
strategies, which we hope will spur additional development in this
relatively unexplored area.",
)
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