COMB: Scalable Concession-Driven Opponent Models Using Bayesian Learning for Preference Learning in Bilateral Multi-Issue Automated Negotiation

IF 3.6 4区 管理学 Q2 MANAGEMENT
Shengbo Chang, Katsuhide Fujita
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Abstract

Learning an opponent’s preferences in bilateral multi-issue automated negotiations can lead to more favorable outcomes. However, existing opponent models can fail in negotiation contexts when their assumptions about opponent behaviors differ from actual behavior patterns. Although integrating broader behavioral assumptions into these models could be beneficial, it poses a challenge because the models are designed with specific assumptions. Therefore, this study proposes an adaptable opponent model that integrates a general behavioral assumption. Specifically, the proposed model uses Bayesian learning (BL), which can apply various behavioral assumptions by considering the opponent’s entire bidding sequence. However, this BL model is computationally infeasible for multi-issue negotiations. Hence, current BL models often impose constraints on their hypothesis space, but these constraints about the utility function’s shape significantly sacrifice accuracy. This study presents a novel scalable BL model that relaxes these constraints to improve accuracy while maintaining linear time complexity by separately learning each parameter of a utility function. Furthermore, we introduce a general assumption that the opponent’s bidding strategy follows a concession-based pattern to enhance adaptability to various negotiation contexts. We explore three likelihood function options to implement this assumption effectively. By incorporating these options into the proposed scalable model, we develop three scalable concession-driven opponent models using Bayesian learning (COMB). Experiments across 45 negotiation domains using 15 basic agents and 15 finalists from the automated negotiating agents competition demonstrate the proposed scalable model’s higher accuracy than existing scalable models. COMB models show higher adaptability to various negotiation contexts than state-of-the-art models.

Abstract Image

COMB:利用贝叶斯学习在双边多问题自动谈判中进行偏好学习的可扩展让步驱动型对手模型
在双边多问题自动谈判中,学习对手的偏好可以带来更有利的结果。然而,当现有的对手模型对对手行为的假设与实际行为模式不同时,就会在谈判中失败。虽然将更广泛的行为假设整合到这些模型中是有益的,但由于模型是根据特定的假设设计的,因此这也是一个挑战。因此,本研究提出了一种整合了一般行为假设的可调整对手模型。具体来说,本研究提出的模型采用贝叶斯学习法(Bayesian Learning,BL),可以通过考虑对手的整个出价序列来应用各种行为假设。然而,这种贝叶斯学习模型对于多问题谈判来说在计算上是不可行的。因此,当前的基本学习模型通常会对其假设空间施加约束,但这些关于效用函数形状的约束会大大牺牲准确性。本研究提出了一种新颖的可扩展 BL 模型,该模型通过分别学习效用函数的每个参数,放宽了这些限制,从而在保持线性时间复杂性的同时提高了准确性。此外,我们还引入了一个一般假设,即对手的出价策略遵循基于让步的模式,以增强对各种谈判环境的适应性。我们探讨了三种有效实现这一假设的似然函数选项。通过将这些选项纳入所提出的可扩展模型,我们利用贝叶斯学习(COMB)开发出了三种可扩展的让步驱动型对手模型。使用 15 个基本代理和 15 个自动谈判代理竞赛决赛选手在 45 个谈判领域进行的实验表明,与现有的可扩展模型相比,所提出的可扩展模型具有更高的准确性。与最先进的模型相比,COMB 模型对各种谈判环境的适应性更高。
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来源期刊
CiteScore
5.70
自引率
6.70%
发文量
32
期刊介绍: The idea underlying the journal, Group Decision and Negotiation, emerges from evolving, unifying approaches to group decision and negotiation processes. These processes are complex and self-organizing involving multiplayer, multicriteria, ill-structured, evolving, dynamic problems. Approaches include (1) computer group decision and negotiation support systems (GDNSS), (2) artificial intelligence and management science, (3) applied game theory, experiment and social choice, and (4) cognitive/behavioral sciences in group decision and negotiation. A number of research studies combine two or more of these fields. The journal provides a publication vehicle for theoretical and empirical research, and real-world applications and case studies. In defining the domain of group decision and negotiation, the term `group'' is interpreted to comprise all multiplayer contexts. Thus, organizational decision support systems providing organization-wide support are included. Group decision and negotiation refers to the whole process or flow of activities relevant to group decision and negotiation, not only to the final choice itself, e.g. scanning, communication and information sharing, problem definition (representation) and evolution, alternative generation and social-emotional interaction. Descriptive, normative and design viewpoints are of interest. Thus, Group Decision and Negotiation deals broadly with relation and coordination in group processes. Areas of application include intraorganizational coordination (as in operations management and integrated design, production, finance, marketing and distribution, e.g. as in new products and global coordination), computer supported collaborative work, labor-management negotiations, interorganizational negotiations, (business, government and nonprofits -- e.g. joint ventures), international (intercultural) negotiations, environmental negotiations, etc. The journal also covers developments of software f or group decision and negotiation.
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