EAGER: Subsequent Similar Cases to Unexpected, Exceptional Cases

EAGER：后续的类似案例和意外的特殊案例

• 批准号: 0938504
• 负责人: Victor Lesser
• 金额: --
• 依托单位: University of Massachusetts Amherst
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-07-01 至 2011-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0938504&HistoricalAwards=false
• 关键词: EAGER; Subsequent; Similar; Cases; Unexpected

Given that change is ubiquitous, it is necessary to understand how systems can learn in response to unexpected, exceptional, and provocative events. This project addresses reasoning about strikingly novel, perhaps disruptive cases, so that subsequent cases similar to the harbinger case do not take an intelligent agent by surprise; rather, the agent can guard against harm and/or lay groundwork to reap reward. The project explores similarity assessment among novel, possibly high-impact cases and uses hypothetical reasoning to explore the space of possible future cases and the implications of these cases on an agent's environment. The extent in time and quality of this exploration (e.g., to include representation change) are conditioned on the costs and benefits associated with anticipating (or not) new events like the original disruptive event.These issues are being explored in two application domains: (1) law where there are numerous areas with apt historical episodes of surprising exceptional cases provoking dramatic change (e.g., warrantless search), and (2) multi-agent distributed resource planning that allow controlled experimentation with models, model-revision policies, and hypothetical problem scenarios (e.g., the Producer-Consumer-Transporter domain). The research is a fundamental step in understanding and representing the actionability facet of concepts, particularly in the face of problematic or atypical instances. It contributes to efforts to promote critical thinking by showing how critical cases can provoke change. It furthers our knowledge of how insightful hypotheticals, so called 'what if' situations, can be used to shed light on the ramifications of actions, classifications, and policy decisions.

鉴于变化无处不在，有必要了解系统如何学习以应对意外、异常和挑衅性事件。该项目致力于对极其新颖、可能具有破坏性的案例进行推理，以便后续类似预兆案例的案例不会让智能代理感到意外；相反，代理人可以防范伤害和/或为获得奖励奠定基础。该项目探索新颖的、可能具有高影响力的案例之间的相似性评估，并使用假设推理来探索未来可能案例的空间以及这些案例对代理环境的影响。这种探索的时间范围和质量（例如，包括表示变化）取决于与预期（或不预期）新事件（如原始破坏性事件）相关的成本和收益。这些问题正在两个应用领域中进行探索：（ 1) 法律，其中有许多领域都有适当的历史事件，令人惊讶的例外情况引发了巨大的变化（例如，无证搜查），以及 (2) 多主体分布式资源规划，允许对模型、模型修订政策进行受控实验，以及假设的问题场景（例如，生产者-消费者-传输者域）。这项研究是理解和表达概念的可操作性方面的一个基本步骤，特别是在面对有问题或非典型的情况时。它通过展示关键案例如何激发变革来促进批判性思维的努力。它加深了我们对如何使用富有洞察力的假设（所谓的“假设”情景）来阐明行动、分类和政策决策的后果的了解。