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摘要翻译：
随着计算代理被开发用于日益复杂的电子商务应用，它们所面临的决策的复杂性要求人工智能技术的发展。例如，在拍卖中代表卖方的代理人应通过对各种可能不确定的信息进行推理，如各种买方可能愿意支付的最高价格、相互竞争的卖方可能提供的价格、拍卖操作的规则、买卖要约的动态到达和匹配，等等，以使卖方的利润最大化。多智能体推理技术的简单应用将要求卖方的智能体在扩展的时间范围内显式地建模所有其他智能体，这使得问题对于许多现实规模的问题来说变得棘手。相反，我们设计了一个新的策略，代理可以用来确定其投标价格，基于拍卖过程的一个更容易处理的马尔可夫链模型。我们已经通过实验确定了我们的新策略工作良好的条件，以及它与agent在知道未来的情况下可以达到的最优性能相比，它的工作有多好。我们的结果表明，我们的新策略总体上表现良好，在大多数实验中优于其他易于操作的启发式策略，并且在有许多购买报价的“卖方市场”中尤其有效。
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英文标题：
《Use of Markov Chains to Design an Agent Bidding Strategy for Continuous
Double Auctions》
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作者：
W. P. Birmingham, E. H. Durfee, S. Park
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最新提交年份：
2011
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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：
As computational agents are developed for increasingly complicated e-commerce applications, the complexity of the decisions they face demands advances in artificial intelligence techniques. For example, an agent representing a seller in an auction should try to maximize the seller's profit by reasoning about a variety of possibly uncertain pieces of information, such as the maximum prices various buyers might be willing to pay, the possible prices being offered by competing sellers, the rules by which the auction operates, the dynamic arrival and matching of offers to buy and sell, and so on. A naive application of multiagent reasoning techniques would require the seller's agent to explicitly model all of the other agents through an extended time horizon, rendering the problem intractable for many realistically-sized problems. We have instead devised a new strategy that an agent can use to determine its bid price based on a more tractable Markov chain model of the auction process. We have experimentally identified the conditions under which our new strategy works well, as well as how well it works in comparison to the optimal performance the agent could have achieved had it known the future. Our results show that our new strategy in general performs well, outperforming other tractable heuristic strategies in a majority of experiments, and is particularly effective in a 'seller?s market', where many buy offers are available.
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PDF链接：
https://arxiv.org/pdf/1106.6022
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