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Defining and Using Ideal Teammate and Opponent Models.
Peter Stone,
Patrick Riley, and Manuela Veloso.
In
Proceedings of the Twelfth Annual Conference on Innovative Applications of Artificial Intelligence, 2000.
AAAI Homepage
[PDF]90.3kB [postscript]101.4kB
A common challenge for agents in multiagent systems is trying to predict what other agents are going to do in the future. Such knowledge can help an agent determine which of its current action options is most likely to achieve its goals. There is a long history in adversarial game playing of using a model of an opponent which assumes that it always acts optimally. Our research extends this strategy to adversarial domains in which the agents have incomplete information, noisy sensors and actuators, and a continuous action space. We introduce ``ideal-model-based behavior outcome prediction'' (IMBBOP) which models the results of other agents' future actions in relation to their optimal actions based on an ideal world model. Our technique also includes a method for relaxing this optimality assumption. IMBBOP was a key component of our successful CMUnited-99 simulated robotic soccer application. In this paper, we define IMBBOP and illustrate its use within the simulated robotic soccer domain. We include empirical results demonstrating the effectiveness of IMBBOP.
@InProceedings{IAAI2000,
author = "Peter Stone and Patrick Riley and Manuela Veloso",
title = "Defining and Using Ideal Teammate and Opponent Models",
BookTitle= "Proceedings of the Twelfth Annual Conference on Innovative Applications of Artificial Intelligence",
year = 2000,
abstract = {A common challenge for agents in multiagent systems
is trying to predict what other agents are going to
do in the future. Such knowledge can help an agent
determine which of its current action options is
most likely to achieve its goals. There is a long
history in adversarial game playing of using a model
of an opponent which assumes that it always acts
optimally. Our research extends this strategy to
adversarial domains in which the agents have
incomplete information, noisy sensors and actuators,
and a continuous action space. We introduce
``ideal-model-based behavior outcome prediction''
(IMBBOP) which models the results of other agents'
future actions in relation to their optimal actions
based on an ideal world model. Our technique also
includes a method for relaxing this optimality
assumption. IMBBOP was a key component of our
successful CMUnited-99 simulated
robotic soccer application. In this paper, we define
IMBBOP and illustrate its use within the simulated
robotic soccer domain. We include empirical results
demonstrating the effectiveness of IMBBOP.},
wwwnote = {<a href=http://www.aaai.org>AAAI Homepage</a>},
}
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