Optimizing Automated Negotiation: Integrating Opponent Modeling with Reinforcement Learning for Strategy Enhancement

Ya Zhang, Jinghua Wu and Ruiyang Cao ()
Additional contact information
Ya Zhang: School of Management, China University of Mining and Technology (Beijing), Beijing 100083, China
Jinghua Wu: School of Management, China University of Mining and Technology (Beijing), Beijing 100083, China
Ruiyang Cao: School of Management, China University of Mining and Technology (Beijing), Beijing 100083, China

Mathematics, 2025, vol. 13, issue 4, 1-30

Abstract: Agent-based automated negotiation aims to enhance decision-making processes by predefining negotiation rules, strategies, and objectives to achieve mutually acceptable agreements. However, most existing research primarily focuses on modeling the formal negotiation phase, while neglecting the critical role of opponent analysis during the pre-negotiation stage. Additionally, the impact of opponent selection and classification on strategy formulation is often overlooked. To address these gaps, we propose a novel automated negotiation framework that enables the agent to use reinforcement learning, enhanced by opponent modeling, for strategy optimization during the negotiation stage. Firstly, we analyze the node and network topology characteristics within an agent-based relational network to uncover the potential strength and types of relationships between negotiating parties. Then, these analysis results are used to inform strategy adjustments through reinforcement learning, where different negotiation strategies are selected based on the opponent’s profile. Specifically, agents’ expectations are adjusted according to relationship strength, ensuring that the expectations of negotiating parties are accurately represented across varying levels of relationship strength. Meanwhile, the relationship classification results are used to adjust the discount factor within a Q-learning negotiation algorithm. Finally, we conducted a series of experiments, and comparative analysis demonstrates that our proposed model outperforms existing negotiation frameworks in terms of negotiation efficiency, utility, and fairness.

Keywords: intelligent agent; automated negotiation; social network analysis; opponent modeling; reinforcement learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/13/4/679/pdf (application/pdf)
https://www.mdpi.com/2227-7390/13/4/679/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:13:y:2025:i:4:p:679-:d:1594850

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().