@InProceedings{kononen05a,
  author =	 {Viller K\"{o}n\"{o}nen},
  title =	 {Hierarchical multiagent reinforcement learning in
                  markov games},
  booktitle =	 {Proceedings of AKRR'05, International and
                  Interdisciplinary Conference on Adaptive Knowledge
                  Representation and Reasoning},
  pages =	 {71--77},
  year =	 2005,
  abstract =	 {Interactions between intelligent agents in
                  multiagent systems can be modeled and analyzed by
                  using game theory. The agents select actions that
                  maximize their utility function so that they also
                  take into account the behavior of the other agents
                  in the system. Each agent should therefore utilize
                  some model of the other agents. In this paper, the
                  focus is on the situation which has a temporal
                  structure and in which the exact form of the
                  interaction between the learning agents is initially
                  unknown and should be learned from the experience.},
  keywords = 	 {multiagent reinforcement learning},
  url = 	 {http://jmvidal.cse.sc.edu/library/kononen05a.pdf},
  cluster = 	 {14324855927659014837}
}