@Article{vidalclri,
  author = 	 {Jos\'{e} M. Vidal and Edmund H. Durfee},
  title = 	 {Predicting the expected behavior of agents that
                  learn about agents: the {CLRI} framework},
  journal = 	 {Autonomous Agents and Multi-Agent Systems},
  abstract = 	 {We describe a framework and equations used to model
                  and predict the behavior of multi-agent systems
                  (MASs) with learning agents. A difference equation
                  is used for calculating the progression of an
                  agent's error in its decision function, thereby
                  telling us how the agent is expected to fare in the
                  MAS. The equation relies on parameters which capture
                  the agent's learning abilities, such as its change
                  rate, learning rate and retention rate, as well as
                  relevant aspects of the MAS such as the impact that
                  agents have on each other. We validate the framework
                  with experimental results using reinforcement
                  learning agents in a market system, as well as with
                  other experimental results gathered from the AI
                  literature. Finally, we use PAC-theory to show how
                  to calculate bounds on the values of the learning
                  parameters},
  url = 	 {http://jmvidal.cse.sc.edu/papers/clri.pdf},
  year = 	 2003,
  month = 	 jan,
  volume = 	 6,
  number = 	 1,
  pages = 	 {77-107},
  arxiv = 	 {cs.MA/0001008},
  doi = 	 {10.1023/A:1021765422660},
  googleid = 	 {jkLuQmPTmJIJ:scholar.google.com/},
  keywords = 	 {multiagent learning},
  cluster = 	 {10563425349275632270}
}