Time Series Predictive Models for Opponent Behavior Modeling in Bilateral Negotiations

Gevher Yesevi*, Mehmet Onur Keskin, Anıl Doğru, Reyhan Aydoğan

*Corresponding author for this work

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

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 languageEnglish
Title of host publicationPRIMA 2022
Subtitle of host publicationPrinciples and Practice of Multi-Agent Systems - 24th International Conference, Proceedings
EditorsReyhan Aydoğan, Natalia Criado, Victor Sanchez-Anguix, Jérôme Lang, Marc Serramia
PublisherSpringer
Pages381-398
Number of pages18
ISBN (Print)9783031212024
DOIs
Publication statusPublished - 2023
Event24th International Conference on Principles and Practice of Multi-Agent Systems, PRIMA 2020 - Valencia , Spain
Duration: 16 Nov 202218 Nov 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13753 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference24th International Conference on Principles and Practice of Multi-Agent Systems, PRIMA 2020
Country/TerritorySpain
CityValencia
Period16/11/2218/11/22

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-care
Otherwise 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

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