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Cobot in LambdaMOO: An Adaptive Social Statistics Agent.
Charles
Lee Isbell, Jr., Michael
Kearns, Satinder Singh, Christian
Shelton, Peter Stone, and Dave
Kormann.
Autonomous Agents and Multiagent Systems, 13(3), November 2006.
JAAMAS
Official
version from publisher's website.
Contains material
that was previously published in an Agents-2001 paper
that won the BEST PAPER AWARD.
(unavailable)
We describe our development of Cobot, a novel software agent who lives in LambdaMOO, a popular virtual world frequented by hundreds of users. Cobot's goal was to become an actual part of that community. Here, we present a detailed discussion of the functionality that made him one of the objects most frequently interacted with in LambdaMOO, human or artificial. Cobot's fundamental power is that he has the ability to collect social statistics summarizing the quantity and quality of interpersonal interactions. Initially, Cobot acted as little more than a reporter of this information; however, as he collected more and more data, he was able to use these statistics as models that allowed him to modify his own behavior. In particular, cobot is able to use this data to "self-program," learning the proper way to respond to the actions of individual users, by observing how others interact with one another. Further, Cobot uses reinforcement learning to proactively take action in this complex social environment, and adapts his behavior based on multiple sources of human reward. Cobot represents a unique experiment in building adaptive agents who must live in and navigate social spaces.
@Article{JAAMAS06,
author="Charles Lee Isbell and Jr. and Michael Kearns and Satinder Singh and Christian Shelton and Peter Stone and Dave Kormann",
title="Cobot in {L}ambda{MOO}: An Adaptive Social Statistics Agent",
journal="Autonomous Agents and Multiagent Systems",
volume="13",number="3",
month="November",
year="2006",
abstract={
We describe our development of Cobot, a novel
software agent who lives in LambdaMOO, a popular
virtual world frequented by hundreds of
users. Cobot's goal was to become an actual part of
that community. Here, we present a detailed
discussion of the functionality that made him one of
the objects most frequently interacted with in
LambdaMOO, human or artificial. Cobot's fundamental
power is that he has the ability to collect social
statistics summarizing the quantity and quality of
interpersonal interactions. Initially, Cobot acted
as little more than a reporter of this information;
however, as he collected more and more data, he was
able to use these statistics as models that allowed
him to modify his own behavior. In particular, cobot
is able to use this data to "self-program," learning
the proper way to respond to the actions of
individual users, by observing how others interact
with one another. Further, Cobot uses reinforcement
learning to proactively take action in this complex
social environment, and adapts his behavior based on
multiple sources of human reward. Cobot represents a
unique experiment in building adaptive agents who
must live in and navigate social spaces.
},
wwwnote={<a href="https://www.springer.com/journal/10458">JAAMAS</a><br>Official version from <a href="https://link.springer.com/article/10.1007/s10458-006-0005-z">publisher's website</a>.<br> Contains material that was previously published in an <a href="http://www.cs.utexas.edu/~pstone/Papers/2001agents/cobots.pdf">Agents-2001 paper</a> that won the <b>BEST PAPER AWARD</b>. },
}
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