Strategic Interactions Among Agents with Bounded Rationality
Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence. Pages 3219-3219.
Abstract
Interactions among agents are complicated since in order to make the best decisions, each agent has to take into account not only the strategy used by other agents but also how those strategies might change in the future (and what causes these changes). The objective of my work will be to develop a framework for learning agent models (opponent or teammate) more accurately and with less interactions, with a special focus on fast learning non-stationary strategies. As preliminary work we have proposed an initial approach for learning non-stationary strategies in repeated games. We use decision trees to learn a model of the agent, and we transform the learned trees into a MDP and solve it to obtain the optimal policy.
@inproceedings{ijcai2013p496,
title = {Strategic Interactions Among Agents with Bounded Rationality},
author = {Hernandez-Leal, Pablo and de Cote, Enrique Munoz and Sucar, Luis Enrique},
booktitle = {Proceedings of the Twenty-Third International Joint Conference on
Artificial Intelligence, {IJCAI-13}},
publisher = {AAAI Press / International Joint Conferences on Artificial Intelligence},
editor = {Francesca Rossi},
pages = {3219--3219},
year = {2013},
month = {8},
url = {https://ijcai.org/proceedings/2013/496},
}
TY - CPAPER TI - Strategic Interactions Among Agents with Bounded Rationality AU - Hernandez-Leal, Pablo AU - de Cote, Enrique Munoz AU - Sucar, Luis Enrique T2 - Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, IJCAI-13 PB - AAAI Press / International Joint Conferences on Artificial Intelligence PY - 2013 SP - 3219 EP - 3219 UR - https://ijcai.org/proceedings/2013/496 ER -Copied
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