Some Connections between Qualitative Spatial Reasoning and Machine Learning

As has been remarked on before, Space is Special[1, 2]. Tobler’s First Law of Geography [3] captures the notion that all things are related, but close things are more related. Tversky [2] eloquently argues for the special place for spatial representations, and in particular that (living) things must move and act in space to survive, that all thought begins as spatial thought and that spatial thinking comes from and is shaped by perceiving the world and acting in it, be it through learning or through evolution. Artificial Intelligence has thus naturally sought to endow artificial agents with spatial representations and ways of reasoning about space. Amongst these, I will focus on qualitative spatial representations and reasoning mechanisms (henceforth QSR, where the ‘R’ may stand for representation or reasoning or both, depending on the context). There have been many calculi developed for representing and reasoning about space in qualitative ways, covering aspects such as (mereo)topology, orientation/direction, size, distance and shape [4, 5]. Whilst QSR has primarily been concerned with deductive reasoning, there have been and there are increasingly many connections between QSR and machine learning. In this talk I will discuss a number of such connections, ranging from the use of qualitative spatial representations in an inductive logic programming system to learn event classes occurring in video data, to the question of whether large language models (LLMs) are able to make inferences reliably about qualitative spatial relations, and whether they can be supported by symbolic reasoners.