Vidal's library
 Title: Recursive Agent Modeling Using Limited Rationality Author: José M. Vidal and Edmund H. Durfee Book Tittle: Proceedings of the First International Conference on Multi-Agent Systems Pages: 125--132 Publisher: AAAI/MIT press Year: 1995 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.

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@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},
}