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Patrick Riley. Classifying Adversarial Behaviors in a Dynamic, Inaccessible, Multi-Agent Environment. Technical Report CMU-CS-99-175, Carnegie Mellon University, 1999.
Developing intelligent agents for multi-agent, inaccessible, adversarial environments is arguably one of the most challenging areas in artificial intelligence today. Great strides have been made in developing emergent cooperation among teammates, but less progress has been made in quickly and automatically changing overall team strategy in response to adversary actions. One way that humans do such adaptation is by noting a similarity to a past adversary. This project is a system to do that sort of classification. The system is fully implemented in the simulated robotic soccer environment as used in RoboCup. The system does the following: Each agent observes the adversary and records relevant features. Based on these observations, each agent then classifies the adversary with regards to a set of predefined behavioral classes. The agents record their classification, and the team classification is decided by a simple majority. The effectiveness of this system on some simple behavior classes is shown. Future directions can include machine learning of behavior classes and strategy changes for those behavior classes, as well as developing more complicated classes.
@TechReport{SeniorThesis,
author = "Patrick Riley",
title = "Classifying Adversarial Behaviors in a Dynamic,
Inaccessible, Multi-Agent Environment",
institution = "Carnegie Mellon University",
year = 1999,
number = "CMU-CS-99-175",
abstract = {Developing intelligent agents for multi-agent,
inaccessible, adversarial environments is arguably
one of the most challenging areas in artificial
intelligence today. Great strides have been made in
developing emergent cooperation among teammates, but
less progress has been made in quickly and
automatically changing overall team strategy in
response to adversary actions. One way that humans
do such adaptation is by noting a similarity to a
past adversary. This project is a system to do that
sort of classification. The system is fully
implemented in the simulated robotic soccer
environment as used in RoboCup. The system does the
following: Each agent observes the adversary and
records relevant features. Based on these
observations, each agent then classifies the
adversary with regards to a set of predefined
behavioral classes. The agents record their
classification, and the team classification is
decided by a simple majority. The effectiveness of
this system on some simple behavior classes is
shown. Future directions can include machine
learning of behavior classes and strategy changes
for those behavior classes, as well as developing
more complicated classes. },
bib2html_pubtype = {Tech Report},
bib2html_rescat = {Opponent and Teammate Modeling},
bib2html_funding = {},
}
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