Abstract
In agent-based negotiations, it is crucial to understand the opponent’s behavior and predict its bidding pattern to act strategically. Foreseeing the utility of the opponent’s coming offer provides valuable insight to the agent so that it can decide its next move wisely. Accordingly, this paper addresses predicting the opponent’s coming offers by employing two deep learning-based approaches: Long Short-Term Memory Networks and Transformers. The learning process has three different targets: estimating the agent’s utility of the opponent’s coming offer, estimating the agent’s utility of that without using opponent-related variables, and estimating the opponent’s utility of that by using opponent-related variables. This work reports the performances of these models that are evaluated in various negotiation scenarios. Our evaluation showed promising results regarding the prediction performance of the proposed methods.
Original language | English |
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Title of host publication | PRIMA 2022 |
Subtitle of host publication | Principles and Practice of Multi-Agent Systems - 24th International Conference, Proceedings |
Editors | Reyhan Aydoğan, Natalia Criado, Victor Sanchez-Anguix, Jérôme Lang, Marc Serramia |
Publisher | Springer |
Pages | 381-398 |
Number of pages | 18 |
ISBN (Print) | 9783031212024 |
DOIs | |
Publication status | Published - 2023 |
Event | 24th International Conference on Principles and Practice of Multi-Agent Systems, PRIMA 2020 - Valencia , Spain Duration: 16 Nov 2022 → 18 Nov 2022 |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 13753 LNAI |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | 24th International Conference on Principles and Practice of Multi-Agent Systems, PRIMA 2020 |
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Country/Territory | Spain |
City | Valencia |
Period | 16/11/22 → 18/11/22 |
Bibliographical note
Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-careOtherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.
Keywords
- Automated negotiation
- Multi-agent systems
- Time-series prediction
- Utility prediction