D-Brane: a diplomacy playing agent for automated negotiations research

de Jonge, D; Sierra, C

D-Brane: a diplomacy playing agent for automated negotiations research

de Jonge, D Sierra, C

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Publication Type:: Journal Article
Citation:: Applied Intelligence, 2017, 47 (1), pp. 158 - 177
Issue Date:: 2017-07-01

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Field	Value	Language
dc.contributor.author	de Jonge, D	en_US
dc.contributor.author	Sierra, C https://orcid.org/0000-0003-0839-6233	en_US
dc.date.issued	2017-07-01	en_US
dc.identifier.citation	Applied Intelligence, 2017, 47 (1), pp. 158 - 177	en_US
dc.identifier.issn	0924-669X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/124114
dc.description.abstract	© 2017, Springer Science+Business Media New York. Existing work on Automated Negotiations commonly assumes the negotiators’ utility functions have explicit closed-form expressions, and can be calculated quickly. In many real-world applications however, the calculation of utility can be a complex, time-consuming problem and utility functions cannot always be expressed in terms of simple formulas. The game of Diplomacy forms an ideal test bed for research on Automated Negotiations in such domains where utility is hard to calculate. Unfortunately, developing a full Diplomacy player is a hard task, which requires more than just the implementation of a negotiation algorithm. The performance of such a player may highly depend on the underlying strategy rather than just its negotiation skills. Therefore, we introduce a new Diplomacy playing agent, called D-Brane, which has won the first international Computer Diplomacy Challenge. It is built up in a modular fashion, disconnecting its negotiation algorithm from its game-playing strategy, to allow future researchers to build their own negotiation algorithms on top of its strategic module. This will allow them to easily compare the performance of different negotiation algorithms. We show that D-Brane strongly outplays a number of previously developed Diplomacy players, even when it does not apply negotiations. Furthermore, we explain the negotiation algorithm applied by D-Brane, and present a number of additional tools, bundled together in the new BANDANA framework, that will make development of Diplomacy-playing agents easier.	en_US
dc.relation.ispartof	Applied Intelligence	en_US
dc.relation.isbasedon	10.1007/s10489-017-0919-y	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	D-Brane: a diplomacy playing agent for automated negotiations research	en_US
dc.type	Journal Article
utslib.citation.volume	1	en_US
utslib.citation.volume	47	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Software
utslib.copyright.status	closed_access
pubs.issue	1	en_US
pubs.publication-status	Published	en_US
pubs.volume	47	en_US

Abstract:

© 2017, Springer Science+Business Media New York. Existing work on Automated Negotiations commonly assumes the negotiators’ utility functions have explicit closed-form expressions, and can be calculated quickly. In many real-world applications however, the calculation of utility can be a complex, time-consuming problem and utility functions cannot always be expressed in terms of simple formulas. The game of Diplomacy forms an ideal test bed for research on Automated Negotiations in such domains where utility is hard to calculate. Unfortunately, developing a full Diplomacy player is a hard task, which requires more than just the implementation of a negotiation algorithm. The performance of such a player may highly depend on the underlying strategy rather than just its negotiation skills. Therefore, we introduce a new Diplomacy playing agent, called D-Brane, which has won the first international Computer Diplomacy Challenge. It is built up in a modular fashion, disconnecting its negotiation algorithm from its game-playing strategy, to allow future researchers to build their own negotiation algorithms on top of its strategic module. This will allow them to easily compare the performance of different negotiation algorithms. We show that D-Brane strongly outplays a number of previously developed Diplomacy players, even when it does not apply negotiations. Furthermore, we explain the negotiation algorithm applied by D-Brane, and present a number of additional tools, bundled together in the new BANDANA framework, that will make development of Diplomacy-playing agents easier.

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http://hdl.handle.net/10453/124114