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@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 = {},
}