Abstract
The research of multiple negotiations considering issue interdependence across negotiations is considered as a complex research topic in agent negotiation. In the multiple negotiations scenario, an agent conducts multiple negotiations with opponents for different negotiation goals, and issues in a single negotiation might be interdependent with issues in other negotiations. Moreover, the utility functions involved in multiple negotiations might be nonlinear, e.g., the issues involved in multiple negotiations are discrete. Considering this research problem, the current work may not well handle multiple interdependent negotiations with complex utility functions, where issues involved in utility functions are discrete. Regarding utility functions involving discrete issues, an agent may not find an offer exactly satisfying its expected utility during the negotiation process. Furthermore, as sub-offers on issues in every single negotiation might be restricted by the interdependence relationships with issues in other negotiations, it is even harder for the agent to find an offer satisfying the expected utility and all involved issue interdependence at the same time, leading to a high failure rate of processing multiple negotiations as a final outcome. To resolve this challenge, this paper presents a negotiation model for multiple negotiations, where interdependence exists between discrete issues across multiple negotiations. By introducing the formal definition of “interdependence between discrete issues across negotiations”, the proposed negotiation model applies the multiple alternating offers protocol, the clustered negotiation procedure and the proposed negotiation strategy to handle multiple interdependent negotiations with discrete issues. In the proposed strategy, the “tolerance value” is introduced as an agent’s consideration to balance between the overall negotiation goal and the negotiation outcomes. The experimental results show that, 1) the proposed model well handles the multiple negotiations with interdependence between discrete issues, 2) the proposed approach is able to help agents in the decision-making process of proposing acceptable offers, 3) an agent can choose a proper “tolerance value” to balance between the success rate of multiple negotiations and its expected utility.
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Acknowledgments
We would like to thank the editors and reviewers for their constructive comments and suggestions to enhance the quality of the paper. This work has been supported in part by the National Natural Science Foundation of China under Grant No. 62006090, and the Fundamental Research Funds for the Central Universities, CCNU under Grant No. 3110120001.
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Lei Niu received the B.Sc. and M.Sc degrees from the University of Electronic Science and Technology of China, Chengdu, China, in 2011 and 2014, respectively. He received the Ph.D. degree from the University of Wollongong (UOW), Wollongong, NSW, Australia, in 2018. He is currently an associate professor with the Central China Normal University Wollongong Joint Institute, Central China Normal University (CCNU), Wuhan, China. His current research interests include multiagent negotiations in open and dynamic environments, multiagent cooperation and coordination, agent-based modelling and simulation in different domains.
Litian Huang received his bachelor’s degree in information management and information system from Wuhan University of Science and Technology, Wuhan, China, in 2017. He is currently pursuing the master’s degree at Central China Normal University Wollongong Joint Institute, Central China Normal University (CCNU), Wuhan, China. His research interests include multi-agent systems, multi-agent learning and intelligent education technology.
Jinhua Zhao received his Ph.D. degree in computer science from University of Clermont Auvergne, France, in 2017. He currently works as a postdoctoral associate at School of Economics and Management, Wuhan University, Wuhan, China. His research interests include combinatorial optimization, algorithms, game theory, evolutionary dynamics and deep learning.
Xinguo Yu is a professor of Faculty of Artificial Intelligence in Education, at Central China Normal University, Wuhan, China, the dean of CCNU Wollongong Joint Institute, and adjunct professor of University of Wollongong, Australia. He is a senior member of both IEEE and ACM. He received a Ph.D. degree in computer science from National University of Singapore. His current research mainly focuses on intelligent educational technology, educational robotics, artificial intelligence, multimedia analysis, computer vision, and virtual reality. He has published over 100 research papers. He is an Associate Editor of International Journal of Digital Crime and Forensics, was Guest Editor of Multimedia Systems and International Journal of Pattern Recognition and Artificial Intelligence. He is general chair of IEEE International Conference on Teaching, Assessment, and Learning for Engineering 2021. He was general chairs and program chairs for more than 10 international conferences in the past 10 years.
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Niu, L., Huang, L., Zhao, J. et al. A Multiple Negotiations Model Considering Interdependence between Discrete Issues Across Negotiations. J. Syst. Sci. Syst. Eng. 30, 417–432 (2021). https://doi.org/10.1007/s11518-021-5495-3
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DOI: https://doi.org/10.1007/s11518-021-5495-3