@InProceedings{	  vidal:95,
  author =	 {Jos\'{e} M. Vidal and Edmund H. Durfee},
  title =	 {Recursive Agent Modeling Using Limited Rationality},
  booktitle =	 {Proceedings of the First International Conference on
                  Multi-Agent Systems},
  publisher = 	 {{AAAI}/{MIT} press},
  year =	 1995,
  pages =	 {125--132},
  abstract =	 {We present an algorithm that an agent can use for
                  determining which of its nested, recursive models of
                  other agents are important to consider when choosing
                  an action. Pruning away less important models allows
                  an agent to take its ``best'' action in a timely
                  manner, given its knowledge, computational
                  capabilities, and time constraints. We describe a
                  theoretical framework, based on {\em situations},
                  for talking about recursive agent models and the
                  strategies and expected strategies associated with
                  them. This framework allows us to rigorously define
                  the {\em gain} of continuing deliberation versus
                  taking action. The expected gain of computational
                  actions is used to guide the pruning of the nested
                  model structure. We have implemented our approach on
                  a canonical multi-agent problem, the pursuit task,
                  to illustrate how real-time, multi-agent
                  decision-making can be based on a principled,
                  combinatorial model. Test results show a marked
                  decrease in deliberation time while maintaining a
                  good performance level.},
  url = 	 {http://jmvidal.cse.sc.edu/papers/vidal95.pdf},
  googleid = 	 {Swxo9jFpVVQJ:scholar.google.com/},
  keywords = 	 {multiagent bounded-rationality},
  cluster = 	 {6076878935514680395}
}