A one-shot bargaining strategy for dealing with multifarious opponents
A one-shot bargaining strategy for dealing with multifarious opponents
复制标题
应对五花八门的对手的一次性讨价还价策略
DOI：
10.1007/s10489-013-0497-6
复制
发表时间：
2014-06-01
影响因子：
5.3
通讯作者：
Leung, Ho-fung
中科院分区：
文献类型：
--
作者：
Ji, Shu-juan;Zhang, Chun-jin;Leung, Ho-fung
讨价还价是解决资源配置问题的有效范式。由于考虑到谈判者的有限理性、时间限制、信息不完全以及动态环境的复杂性等因素，一次性讨价还价的最优策略设计比假设所有讨价还价者都绝对理性的情况要困难得多。许多基于预测的策略已经被探索，或者基于假设有限数量的模型为对手，或专注于预测对手的保留价，截止日期，或不同行为的概率。本文在分析对手隐私信息估计方法的基础上，提出了一种改进的BLGAN策略，以适应各种可能的讨价还价情况，并能有效地应对多样化的对手。此外，本文还将改进的BLGAN策略与相关工作进行了比较。实验结果表明，改进后的BLGAN策略在面对不同的对手时，尤其是面对频繁改变策略进行反学习的智能体时，其性能优于相关策略。
Bargaining is an effective paradigm to solve the problem of resource allocation. The consideration of factors such as bounded rationality of negotiators, time constraints, incomplete information, and complexity of dynamic environment make the design of optimal strategy for one-shot bargaining much tougher than the situation that all bargainers are assumed to be absolutely rational. Lots of prediction-based strategies have been explored either based on assuming a finite number of models for opponents, or focusing on the prediction of opponent’s reserve price, deadline, or the probabilities of different behaviors. Following the methods of estimating opponent’s private information, this paper gives a strategy which improves the BLGAN strategy to adapt to various possible bargaining situations and deal with multifarious opponents. In addition, this paper compares the improved BLGAN strategy with related work. Experimental results show that the improved BLGAN strategy can outperform related ones when faced with various opponents, especially the agents who frequently change their strategies for anti-learning.