新颖性形式理论的统一框架：框架、实例与讨论
A Unifying Framework for Formal Theories of Novelty:Framework, Examples and Discussion
摘要：
当智能体从实验室走向开放世界时，管理新的、未知的或非分布的输入至关重要。新颖性相关的问题包括容忍正常输入的新颖扰动，检测输入何时包括新颖项，以及适应新颖输入。虽然在这些领域进行了大量的研究，但在缺乏超越问题领域的新颖性的正式定义方面存在着明显的差距。作为一个跨多个研究小组和不同领域的研究团队，我们已经亲眼目睹了由于不明确的新颖性问题以及不一致的定义和术语而产生的困难。因此，我们提出了第一个统一的框架，新颖性的正式理论，并使用该框架正式定义了一个家庭的新颖性类型。我们的框架可以应用于广泛的领域，从符号人工智能到强化学习，再到开放世界的图像识别。因此，它可以用来帮助启动新的研究工作，并加速正在进行的工作，这些重要的新颖性相关的问题。我们的AAAI 2021论文的扩展版本包括多个领域的更多细节和示例。
英文摘要：
Managing inputs that are novel, unknown, or out-of-distribution is critical as an agent moves from the lab to the open world. Novelty-related problems include being tolerant to novel perturbations of the normal input, detecting when the input includes novel items, and adapting to novel inputs. While significant research has been undertaken in these areas, a noticeable gap exists in the lack of a formalized definition of novelty that transcends problem domains. As a team of researchers spanning multiple research groups and different domains, we have seen, first hand, the difficulties that arise from ill-specified novelty problems, as well as inconsistent definitions and terminology. Therefore, we present the first unified framework for formal theories of novelty and use the framework to formally define a family of novelty types. Our framework can be applied across a wide range of domains, from symbolic AI to reinforcement learning, and beyond to open world image recognition. Thus, it can be used to help kick-start new research efforts and accelerate ongoing work on these important novelty-related problems. This extended version of our AAAI 2021 paper included more details and examples in multiple domains.
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。
