利用声誉感知决策支持技术构建健壮的众包系统
Building Robust Crowdsourcing Systems with Reputation-aware Decision Support Techniques
摘要：
众包是指从一大群无关的人那里征求贡献的安排。由于这一性质，众包者（或任务请求者）常常面临工作人员能力的不确定性，这反过来又影响所获得结果的质量和及时性。信任是人们用来促进人类社会互动的一种机制，在人类社会中，风险和不确定性是常见的。建立一个强大的众包系统的关键挑战是如何使信任意识的任务授权决策，有效地利用工人（或受托代理人）的能力，以实现高社会福利？本书介绍了应对这一挑战的研究。它超越了现有的信任管理研究框架，删除了一个广泛的假设隐含通过现有的研究：受托人代理可以处理无限数量的交互请求每离散的时间单位，而不会损害其性能的任务请求者（或信任代理）。基于拥塞博弈原理，将众包决策支持问题转化为多主体信任博弈问题，提出了两种信任感知交互决策方法：社会福利声誉感知决策优化方法（SWORD）和信任主体公平利用分布式请求接受方法（DRAFT）。SWORD是为集中式系统设计的，而DRAFT是为完全分布式系统设计的。理论分析表明，这两种方法产生的社会福利可以通过调整一个关键参数而更接近最优。有了这两种方法，众包系统的研究框架可以得到丰富，以处理更现实的情况下，工人有不同的和有限的能力。
英文摘要：
Crowdsourcing refers to the arrangement in which contributions are solicited from a large group of unrelated people. Due to this nature, crowdsourcers (or task requesters) often face uncertainty about the workers' capabilities which, in turn, affects the quality and timeliness of the results obtained. Trust is a mechanism used by people to facilitate interactions in human societies where risk and uncertain are common. The crucial challenge to building a robust crowdsourcing system is how to make trust-aware task delegation decisions to efficiently utilize the capacities of workers (or trustee agents) to achieve high social welfare? This book presents the research addressing this challenge. It goes beyond the existing trust management research framework by removing a widespread assumption implicitly adopted by existing research: that a trustee agent can process an unlimited number of interaction requests per discrete time unit without compromising its performance as perceived by the task requesters (or truster agents). Decision support in crowdsourcing is re-formalized as a multi-agent trust game based on the principles of the Congestion Game, which is solved by two trust-aware interaction decision-making approaches: 1) the Social Welfare Optimizing approach for Reputation-aware Decision-making (SWORD) approach, and 2) the Distributed Request Acceptance approach for Fair utilization of Trustee agents (DRAFT). SWORD is designed for centralized systems, while DRAFT is designed for fully distributed systems. Theoretical analyses have shown that the social welfare produced by these two approaches can be made closer to optimal by adjusting only one key parameter. With these two approaches, the framework of research for crowdsourcing systems can be enriched to handle more realistic scenarios where workers have varied and limited capabilities.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
