EAGER: Subsequent Similar Cases to Unexpected, Exceptional Cases

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。这个项目解决了关于惊人的新颖性，也许是破坏性的情况下，推理，使后续的情况下类似的先兆的情况下不采取智能代理的惊喜;相反，代理可以防止伤害和/或奠定基础，以获得奖励。该项目探讨了新的，可能具有高影响力的情况下，相似性评估，并使用假设推理，以探索未来可能的情况下，这些情况下对代理人的环境的影响的空间。这种探索的时间和质量的程度（例如，这些问题在两个应用领域中被探索：（1）法律领域，其中存在许多具有引起戏剧性变化的令人惊讶的例外情况的恰当历史情节（例如，无障碍搜索），以及（2）多代理分布式资源规划，其允许对模型、模型修订策略和假设问题场景（例如，生产者-消费者-运输者域）。研究是理解和代表概念的可操作性方面的基本步骤，特别是在面对有问题或非典型的情况下。它有助于通过展示关键案例如何引发变革来促进批判性思维。它进一步加深了我们的知识，如何洞察假设，所谓的“如果”的情况下，可以用来阐明行动，分类和政策决定的后果。

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.