RI: Small: Agent-Assisted Trading in Real-World Auctions

在许多大型经济市场中，商品通过拍卖出售。例子包括eBay、无线频谱拍卖、纽约证券交易所和荷兰花卉拍卖。这个项目专注于一个特定的现实世界的拍卖领域：荷兰花卉拍卖（DFA）。该项目将从竞标者的角度和拍卖商的角度对这一领域进行深入分析。这个项目包括几个组成部分的任务。(1)创建一个可配置的拍卖服务器，包括基本的拍卖构建模块，如同步和顺序拍卖，密封出价，上升和下降拍卖。(2)使用可配置的拍卖服务器构建交互式DFA仿真，该服务器能够处理由人类竞买者和自主代理做出的决策；该模拟将模拟DFA投标人的偏好，这些偏好将使用历史数据中的逆强化学习技术自动学习，并通过调查和采访DFA专家来手动学习。(3)建立自主DFA投标代理，并利用交互仿真将其与人类DFA投标专家的表现进行比较。(4)进一步对这些agent进行仿真，经验地找到在不同拍卖参数设置下的博弈论均衡，从而对这些参数进行优化。该项目还将开发一个混合倡议系统的原型，可用于协助DFA投标人。这项研究的潜在更广泛的影响包括增加对如何设计和实施人工智能代理的知识和理解，这些代理可以有效地帮助人类做出决策，特别是在信息丰富和时间紧迫的环境中。尽管该项目侧重于dfa类型的拍卖，但这里开发的方法将转移到使用拍卖机制的其他领域，例如智能电网中资源分配的自动竞标。对DFA的详细关注为研究各种与拍卖相关的主题提供了具体的起点，例如，如何构建混合主动决策支持工具以提高拍卖实践的质量，严格比较各种拍卖机制和参数选择，以及将招标代理设计从启发式方法推进到有理论依据的方法。项目研究团队包括研究生和本科生，他们参与这项研究将加强他们对决策支持系统发展至关重要的各个领域的理解（例如，自主代理，偏好激发和表示，机器学习，软件工程），并为他们提供跨学科合作的经验，并致力于现实世界的应用。此外，还计划在Artemis项目中开发关于自主招标代理的一般主题的入门课程，这是一个为期五周的暑期项目，在这个项目中，九年级的女学生将接触到计算机科学的广泛应用，并被介绍到各种计算基础技术。

In many large economic markets, goods are sold through auctions. Examples include eBay, wireless spectrum auctions, the New York Stock Exchange, and the Dutch flower auctions. This project focuses on one particular real-world auction domain: the Dutch flower auctions (DFA). The project will perform an in-depth analysis of this domain both from the point of view of the bidders and from the point of view of the auctioneers.This project involves several component tasks. (1) Creation of a configurable auction server, comprised of basic auction building blocks, like simultaneous and sequential auctions, and sealed-bid, ascending, and descending auctions. (2) Construction of an interactive DFA simulation using the configurable auction server that is capable of handling decisions made by both human bidders and autonomous agents; this simulation will model DFA bidder preferences, which will be learned automatically using inverse reinforcement learning techniques from historical data and manually by surveying and interviewing DFA experts. (3) Creation of autonomous DFA bidding agents and comparison of their performance to that of expert human DFA bidders using the interactive simulation. (4) Further simulation of these agents to empirically find game-theoretic equilibria under various settings of the auction parameters so that those parameters can be optimized. The project will also develop a prototype of a mixed-initiative system that can be used to assist DFA bidders.Potential broader impacts of this research include increased knowledge and understanding of how to design and implement artificially intelligent agents that can effectively assist humans with their decision-making efforts, particularly in information-rich and time-critical environments. Although this project focuses on DFA-type auctions, methodologies developed here will transfer to other domains that use auction mechanisms, such as automated bidding for resource allocation in smart grids. Detailed focus on the DFA provides a concrete starting point for the study of various auction-related topics, for instance, how to build mixed-initiative decision support tools to improve the quality of bidding practices, rigorous comparison of various auction mechanisms and parameter choices, and advancing bidding agent design from a heuristic approach to one that is theoretically grounded.The project research team includes both graduate and undergraduate students whose participation in this research will strengthen their understanding of the various fields that are critical to the development of decision support systems (e.g., autonomous agents, preference elicitation and representation, machine learning, software engineering) and provide them experience collaborating across disciplines, and working on a real-world application. In addition, there are plans to develop introductory lessons on the general topic of autonomous bidding agents for use in the Artemis project, a five week summer program in which female rising ninth graders are exposed to the breadth of applications of computer science, and are introduced to a variety of technologies underlying computing.