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首页> 外文期刊>International Journal of Intelligent Systems >Preface to the Special Issue on Learning Approaches for Negotiation Agents and Automated Negotiation
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Preface to the Special Issue on Learning Approaches for Negotiation Agents and Automated Negotiation

机译：谈判代理和自动谈判方法学习专刊的序言

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摘要

Learning is required for enhancing the performance of agents in a situation when there is no complete information about the preferences or decision-making processes of other agents. Negotiation agents and dynamic pricing agents often do not have complete information about the preferences, deadline, reserved price, and decision-making processes of other agents. In automated negotiation as well as dynamic pricing systems, learning is essential because in adjusting and adapting their strategies during negotiation and trading, agents are more likely to achieve better outcomes and increase their payoffs. Even though there are many existing e-negotiation agents for e-commerce, many of them were not designed with the ability to adapt and enhance their strategies through learning. Learning approaches such as reinforcement learning, belief-based learning, and evolutionary learning can potentially enhance the performance of negotiation as well as dynamic pricing agents. Negotiation agents using reinforcement learning enhance their performance through trial-and-error interactions with other agents. In belief-based learning, agents keep track of the history of previous actions of other agents and form beliefs about what other agents will do in the future based on past observations. Based on these observations, they tend to select a best-response strategy that maximizes their expected payoffs given the beliefs they have formed. In evolutionary learning, agents evolve and derive effective strategies using genetic algorithms. This special issue brings together researchers in multiagent learning, automated negotiation, and game-theoretic approaches of multiagent systems to present the latest research results in developing advanced negotiation agents as well as dynamic pricing systems. It serves to highlight recent research achievements in multiagent learning techniques and Pareto-search methods for automated negotiation and dynamic pricing systems.
机译：在没有其他代理人的偏好或决策过程的完整信息的情况下，需要学习以增强代理人的绩效。谈判代理和动态定价代理通常没有关于其他代理的偏好，截止日期，保留价格和决策过程的完整信息。在自动协商以及动态定价系统中，学习是必不可少的，因为在协商和交易期间调整和调整其策略时，代理商更有可能取得更好的结果并增加收益。即使现有许多用于电子商务的电子谈判代理，但其中许多设计都不具备通过学习来适应和增强其策略的能力。强化学习，基于信念的学习和进化学习等学习方法可以潜在地提高谈判以及动态定价代理的绩效。使用强化学习的谈判代理通过与其他代理的试错互动来增强其性能。在基于信念的学习中，行为者会跟踪其他行为者的先前行为的历史，并基于过去的观察结果形成对其他行为者将来会做什么的信念。根据这些观察结果，他们倾向于选择一种最佳响应策略，从而在给定信念的情况下将预期收益最大化。在进化学习中，主体利用遗传算法进化并得出有效的策略。本期特刊将研究人员集中在多代理学习，自动协商以及多代理系统的博弈论方法上，以介绍开发高级协商代理和动态定价系统的最新研究成果。它着重介绍了在多主体学习技术和自动协商和动态定价系统的Pareto搜索方法方面的最新研究成果。

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