Bayesian-based preference prediction in bilateral multi-issue negotiation between intelligent agents

Zhang, Jihang; Ren, Fenghui; Zhang, Minjie

doi:10.1016/j.knosys.2015.04.006

Cited by 25 publications

(11 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…From this study, we can notice that only some approaches have focused on negotiation in the cloud context such as References During negotiations, and especially in competitive negotiation environments, information about agent preferences may not be revealed and shared publicly. For this reason, different methods are proposed in the literature that can predict the preferences of opponents such as prediction based on genetic algorithms, 9 prediction based on Bayes' theorem 4 and prediction based on the machine learning. 10 However, these methods have certain limitations, such as the need for prior knowledge to formulate hypotheses on the opponent's preferences in the case of the prediction based on Bayes' theorem and the need for several learning steps before giving effective results in the case of the prediction based on genetic algorithms.…”

Section: Related Workmentioning

confidence: 99%

A negotiation framework for the cloud using rough set theory‐based preference prediction

Malouche

Halima

Ghézala

2022

Concurrency and Computation

View full text Add to dashboard Cite

Summary In recent years, cloud computing has become a priority for organizations that seek to facilitate the management of their increasingly complex information systems (IS) that includes different components: data, services, business processes and hardware. With the large number of cloud providers, the selection of cloud services for each IS component remains a challenge because each one has its own requirements in terms of quality of service which may be different from each other. Cloud providers preferences are generally different from those of organizations, hence the need for a negotiation process. In this article, we propose a framework on which the negotiations between organizations and cloud providers will be based. In this framework, we use rough set theory to predict provider preferences. This method plays an important role in improving the results of negotiations and allows to speed up this process since the preferences of the providers will be known. Additionally, we propose an improvement to an existing negotiation strategy in order to further speed up negotiation process and increase organization utility. Experiments show the effectiveness of our approach in terms of utility, time and success rate.

show abstract

Section: Related Workmentioning

confidence: 99%

A negotiation framework for the cloud using rough set theory‐based preference prediction

Malouche

Halima

Ghézala

2022

Concurrency and Computation

View full text Add to dashboard Cite

show abstract

“…Most of the work on learning in negotiation has focused significantly on learning the characteristics of the opponent agents. Hindriks et al [20], Buffett and Spencer [9], Yu et al [56], Zhang et al [58], Li and Cao [29], and Zeng and Sycara [52,57] propose methods that build on Bayesian learning to estimate the opponent's preferences. A similar method is used by Ren and Anumba [44], and Zhang et al [59] to learn the opponent's acceptance strategy by estimating the reservation value from previous negotiations.…”

Section: Related Workmentioning

confidence: 99%

ANEGMA: an automated negotiation model for e-markets

Bagga

Paoletti

Alrayes

et al. 2021

Auton Agent Multi-Agent Syst

View full text Add to dashboard Cite

We present a novel negotiation model that allows an agent to learn how to negotiate during concurrent bilateral negotiations in unknown and dynamic e-markets. The agent uses an actor-critic architecture with model-free reinforcement learning to learn a strategy expressed as a deep neural network. We pre-train the strategy by supervision from synthetic market data, thereby decreasing the exploration time required for learning during negotiation. As a result, we can build automated agents for concurrent negotiations that can adapt to different e-market settings without the need to be pre-programmed. Our experimental evaluation shows that our deep reinforcement learning based agents outperform two existing well-known negotiation strategies in one-to-many concurrent bilateral negotiations for a range of e-market settings.

show abstract

“…In order to better promote the agent's self-adaptive negotiation ability, an army of scholars have begun to introduce machine learning into the negotiation. Bayesian Learning estimates the probability distribution of opponent negotiation parameters and preferences and adaptively adjusts the concession strategy [13]. Q-Learning generates the optimal negotiation strategy by calculating the utility cumulative value [14].…”

Section: Literature Reviewmentioning

confidence: 99%

“…The error between the predictive utility value in round t+1 and actual utility value in round t can be calculated by coordinating equations (12) and (13). While Δ +1, > 0, the utility of concession has not been maximized; it will increase.…”

Section: Concessional Learning Based On Dynamic Selectivementioning

confidence: 99%

A Negotiation Optimization Strategy of Collaborative Procurement with Supply Chain Based on Multi-Agent System

Chen

2018

Mathematical Problems in Engineering

View full text Add to dashboard Cite

In the process of collaborative procurement, buyers and suppliers are prone to conflict in cooperation due to differences in needs and preferences. Negotiation is a crucial way to resolve the conflict. Aimed at ameliorating the situations of underdeveloped self-adaptive learning effect of current collaborative procurement negotiation, this paper constructs a negotiation model based on multi-agent system and proposes a negotiation optimization strategy combined with machine learning. It provides a novel perspective for the analysis of intelligent SCM. The experimental results suggest that the proposed strategy improves the success rate of self-adaptive learning and joint utility of agents compared with the strategy of single learning machine, and it achieves win-win cooperation between purchasing enterprise and supplier.

show abstract

Bayesian-based preference prediction in bilateral multi-issue negotiation between intelligent agents

Cited by 25 publications

References 28 publications

A negotiation framework for the cloud using rough set theory‐based preference prediction

A negotiation framework for the cloud using rough set theory‐based preference prediction

ANEGMA: an automated negotiation model for e-markets

A Negotiation Optimization Strategy of Collaborative Procurement with Supply Chain Based on Multi-Agent System

Contact Info

Product

Resources

About