Conditional Restricted Boltzmann Machines for Negotiations in Highly Competitive and Complex Domains
Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence. Pages 69-74.
Abstract
Learning in automated negotiations, while useful, is hard because of the indirect way the target function can be observed and the limited amount of experience available to learn from. This paper proposes two novel opponent modeling techniques based on deep learning methods. Moreover, to improve the learning efficacy of negotiating agents, the second approach is also capable of transferring knowledge efficiently between negotiation tasks. Transfer is conducted by automatically mapping the source knowledge to the target in a rich feature space. Experiments show that using these techniques the proposed strategies outperform existing state-of-the-art agents in highly competitive and complex negotiation domains. Furthermore, the empirical game theoretic analysis reveals the robustness of the proposed strategies.
@inproceedings{ijcai2013p22,
title = {Conditional Restricted Boltzmann Machines for Negotiations in Highly Competitive and Complex Domains},
author = {Chen, Siqi and Bou-Ammar, Haitham and Tuyls, Karl and Weiss, Gerhard},
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 = {69--74},
year = {2013},
month = {8},
url = {https://ijcai.org/proceedings/2013/22},
}
TY - CPAPER TI - Conditional Restricted Boltzmann Machines for Negotiations in Highly Competitive and Complex Domains AU - Chen, Siqi AU - Bou-Ammar, Haitham AU - Tuyls, Karl AU - Weiss, Gerhard 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 - 69 EP - 74 UR - https://ijcai.org/proceedings/2013/22 ER -Copied
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