摘要翻译:
研究了组合域中基于Agent的协商问题。在双边或多边谈判中,当代理人对可能的替代方案的偏好不是公共知识时,很难达成最优协议。在这种情况下,利己的代理人往往会谈判低效的协议。本文给出了一个在组合域中的协商协议,该协议可以引导理性Agent在不完全信息条件下达成最优协议。我们提出的协议使得协商代理能够使用只访问整个结果空间的一小部分子空间的分布式搜索来识别有效的解决方案。此外,该协议具有足够的通用性,适用于组合领域中的大多数偏好表示模型。我们也给出了实验结果,证明了我们的方法的可行性和计算效率。
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英文标题:
《An Efficient Protocol for Negotiation over Combinatorial Domains with
Incomplete Information》
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作者:
Minyi Li, Quoc Bao Vo, Ryszard Kowalczyk
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最新提交年份:
2012
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分类信息:
一级分类:Computer Science 计算机科学
二级分类:Artificial Intelligence 人工智能
分类描述:Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域,除了视觉、机器人、机器学习、多智能体系统以及计算和语言(自然语言处理),这些领域有独立的学科领域。特别地,包括专家系统,定理证明(尽管这可能与计算机科学中的逻辑重叠),知识表示,规划,和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要:
We study the problem of agent-based negotiation in combinatorial domains. It is difficult to reach optimal agreements in bilateral or multi-lateral negotiations when the agents' preferences for the possible alternatives are not common knowledge. Self-interested agents often end up negotiating inefficient agreements in such situations. In this paper, we present a protocol for negotiation in combinatorial domains which can lead rational agents to reach optimal agreements under incomplete information setting. Our proposed protocol enables the negotiating agents to identify efficient solutions using distributed search that visits only a small subspace of the whole outcome space. Moreover, the proposed protocol is sufficiently general that it is applicable to most preference representation models in combinatorial domains. We also present results of experiments that demonstrate the feasibility and computational efficiency of our approach.
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PDF链接:
https://arxiv.org/pdf/1202.3740