Knowledge about space and time is necessary to solve problems in the physical
world: An AI agent situated in the physical world and interacting with objects
often needs to reason about positions of and relations between objects; and as
soon as the agent plans its actions to solve a task, it needs to consider the
temporal aspect (e.g., what actions to perform over time). Spatio-temporal
knowledge, however, is required beyond interacting with the physical world, and
is also often transferred to the abstract world of concepts through analogies
and metaphors (e.g., "a threat that is hanging over our heads"). As spatial and
temporal reasoning is ubiquitous, different attempts have been made to
integrate this into AI systems. In the area of knowledge representation,
spatial and temporal reasoning has been largely limited to modeling objects and
relations and developing reasoning methods to verify statements about objects
and relations. On the other hand, neural network researchers have tried to
teach models to learn spatial relations from data with limited reasoning
capabilities. Bridging the gap between these two approaches in a mutually
beneficial way could allow us to tackle many complex real-world problems, such
as natural language processing, visual question answering, and semantic image
segmentation. In this chapter, we view this integration problem from the
perspective of Neuro-Symbolic AI. Specifically, we propose a synergy between
logical reasoning and machine learning that will be grounded on spatial and
temporal knowledge. Describing some successful applications, remaining
challenges, and evaluation datasets pertaining to this direction is the main
topic of this contribution.

本文介绍了如何通过在神经符号人工智能方向上融合逻辑推理和机器学习，来整合空间和时间知识，解决复杂的现实问题，包括自然语言处理、视觉问题回答和语义图像分割。