Using Gaussian Processes to Optimise Concession in Complex Negotiations against Unknown Opponents
Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence. Pages 432-437.
Abstract
In multi-issue automated negotiation against unknown opponents, a key part of effective negotiation is the choice of concession strategy. In this paper, we develop a principled concession strategy, based on Gaussian processes predicting the opponent's future behaviour. We then use this to set the agent's concession rate dynamically during a single negotiation session. We analyse the performance of our strategy and show that it outperforms the state-of-the-art negotiating agents from the 2010 Automated Negotiating Agents Competition, in both a tournament setting and in self-play, across a variety of negotiation domains.
@inproceedings{ijcai2011p80,
title = {Using Gaussian Processes to Optimise Concession in Complex Negotiations against Unknown Opponents},
author = {Williams, Colin R. and Robu, Valentin and Gerding, Enrico H. and Jennings, Nicholas R.},
booktitle = {Proceedings of the Twenty-Second International Joint Conference on
Artificial Intelligence, {IJCAI-11}},
publisher = {AAAI Press/International Joint Conferences on Artificial Intelligence},
editor = {Toby Walsh},
pages = {432--437},
year = {2011},
month = {7},
doi = {10.5591/978-1-57735-516-8/IJCAI11-080},
url = {https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-080},
}
TY - CPAPER TI - Using Gaussian Processes to Optimise Concession in Complex Negotiations against Unknown Opponents AU - Williams, Colin R. AU - Robu, Valentin AU - Gerding, Enrico H. AU - Jennings, Nicholas R. T2 - Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, IJCAI-11 PB - AAAI Press/International Joint Conferences on Artificial Intelligence PY - 2011 SP - 432 EP - 437 DO - 10.5591/978-1-57735-516-8/IJCAI11-080 UR - https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-080 ER -Copied
Machine-readable versions of this paper, for tools and AI agents: