@Unpublished{lerman04a,
  author =	 {Kristina Lerman and Aram Galstyan and Tad Hogg},
  title =	 {Mathematical Analysis of Multi-Agent Systems},
  googleid =	 {MCuYSrQrK0EJ:scholar.google.com/},
  arxiv =	 {cs.RO/0404002},
  year =	 {2004},
  abstract =	 {We review existing approaches to mathematical
                  modeling and analysis of multi-agent systems in
                  which complex collective behavior arises out of
                  local interactions between many simple
                  agents. Though the behavior of an individual agent
                  can be considered to be stochastic and
                  unpredictable, the collective behavior of such
                  systems can have a simple probabilistic
                  description. We show that a class of mathematical
                  models that describe the dynamics of collective
                  behavior of multi-agent systems can be written down
                  from the details of the individual agent
                  controller. The models are valid for Markov or
                  memoryless agents, in which each agents future state
                  depends only on its present state and not any of the
                  past states. We illustrate the approach by analyzing
                  in detail applications from the robotics domain:
                  collaboration and foraging in groups of robots.},
  keywords =     {multiagent modeling},
  url =		 {http://xxx.lanl.gov/abs/cs.RO/0404002},
  comment =	 {masrg},
  cluster = 	 {4695895089809468208}
}