An adaptive bilateral negotiation model for e-commerce settings

V. Narayanan, N. Jennings
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引用次数: 55

Abstract

This paper studies adaptive bilateral negotiation between software agents in e-commerce environments. Specifically, we assume that the agents are self-interested, the environment is dynamic, and both agents have deadlines. Such dynamism means that the agents' negotiation parameters (such as deadlines and reservation prices) are functions of both the state of the encounter and the environment. Given this, we develop an algorithm that the negotiating agents can use to adapt their strategies to changes in the environment in order to reach an agreement within their specific deadlines and before the resources available for negotiation are exhausted. In more detail, we formally define an adaptive negotiation model and cast it as a Markov decision process. Using a value iteration algorithm, we then indicate a novel solution technique for determining optimal policies for the negotiation problem without explicit knowledge of the dynamics of the system. We also solve a representative negotiation decision problem using this technique and show that it is a promising approach for analyzing negotiations in dynamic settings. Finally, through empirical evaluation, we show that the agents using our algorithm learn a negotiation strategy that adapts to the environment and enables them to reach agreements in a timely manner.
电子商务环境下的自适应双边谈判模型
本文研究了电子商务环境下软件代理之间的自适应双边协商问题。具体来说,我们假设代理是自利的,环境是动态的,并且两个代理都有截止日期。这种动态意味着代理的谈判参数(如截止日期和预订价格)是相遇状态和环境的函数。鉴于此,我们开发了一种算法,谈判代理可以使用该算法来调整其策略以适应环境的变化,以便在其特定的最后期限内并在可用的谈判资源耗尽之前达成协议。更详细地说,我们正式定义了一个自适应协商模型,并将其转换为马尔可夫决策过程。使用值迭代算法,我们指出了一种新的解决技术,用于确定协商问题的最优策略,而无需明确了解系统的动力学。我们还使用该技术解决了一个有代表性的谈判决策问题,并表明它是一种有前途的方法来分析动态环境下的谈判。最后,通过实证评估,我们表明使用我们算法的代理学习了一种适应环境的谈判策略,并使他们能够及时达成协议。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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