“Open world” environments are those in which novel objects, agents, events, and more can appear and contradict previous understandings of the environment. This runs counter to the “closed world” assumption used in most AI research, where the environment is assumed to be fully understood and unchanging. The types of environments AI agents can be deployed in are limited by the inability to handle the novelties that occur in open world environments. This paper presents a novel cognitive architecture framework to handle open-world novelties. This framework combines symbolic planning, counterfactual reasoning, reinforcement learning, and deep computer vision to detect and accommodate novelties. We introduce general algorithms for exploring open worlds using inference and machine learning methodologies to facilitate novelty accommodation. The ability to detect and accommodate novelties allows agents built on this framework to successfully complete tasks despite a variety of novel changes to the world. Both the framework components and the entire system are evaluated in Minecraft-like simulated environments. Our results indicate that agents are able to efficiently complete tasks while accommodating “concealed novelties” not shared with the architecture development team.

中文翻译：

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用于处理开放世界中的新事物的神经符号认知架构框架

“开放世界”环境是指新的物体、代理、事件等可能出现并与之前对环境的理解相矛盾的环境。这与大多数人工智能研究中使用的“封闭世界”假设背道而驰，其中环境被假设为完全理解且不变。由于无法处理开放世界环境中出现的新奇事物，人工智能代理可以部署的环境类型受到限制。本文提出了一种新颖的认知架构框架来处理开放世界的新奇事物。该框架结合了符号规划、反事实推理、强化学习和深度计算机视觉来检测和适应新奇事物。我们介绍使用推理和机器学习方法探索开放世界的通用算法，以促进新奇的适应。尽管世界发生了各种新奇的变化，但基于此框架构建的智能体能够检测和适应新奇事物，从而成功完成任务。框架组件和整个系统都在类似 Minecraft 的模拟环境中进行评估。我们的结果表明，代理能够有效地完成任务，同时适应不与架构开发团队共享的“隐藏的新奇事物”。