A schematic of a biological system, ie, a representation of its pieces, how they are combined,
and what they do, would facilitate understanding its essential organization and alteration in …

Concept-based XAI (C-XAI) approaches to explaining neural vision models are a promising
field of research, since explanations that refer to concepts (ie, semantically meaningful parts …

An argument that qualitative representations—symbolic representations that carve
continuous phenomena into meaningful units—are central to human cognition. In this book …

Qualitative spatial/temporal reasoning (QSR/QTR) plays a key role in research on human
cognition, eg, as it relates to navigation, as well as in work on robotics and artificial …

Probabilistic logics combine the ability to reason about complex scenes, with a rigorous
approach to uncertainty. This paper explores the construction of probabilistic spatial logics …

Abstract Qualitative Spatial & Temporal Reasoning (QSTR) is a major field of study in
Symbolic AI that deals with the representation and reasoning of spatiotemporal information …

We study the complexity of infinite-domain constraint satisfaction problems (CSPs): our basic
setting is that a complexity classification for the CSPs of first-order expansions of a structure …

This paper reviews formal approaches to representing spatial knowledge about qualitative
direction. Unlike geometric direction information, qualitative information does not employ …

Taken literally, geoAI is the use of Artificial Intelligence methods and techniques in solving
geo-spatial problems. Similar to AI more generally, geoAI has seen an influx of new (big) …

Automated vehicles (AV) heavily depend on robust perception systems. Current methods for
evaluating vision systems focus mainly on frame-by-frame performance. Such evaluation …