Special Issue on "Landmark-Based Navigation in Cognitive Systems"

Künstliche Intelligenz – German Journal on Artificial Intelligence

The importance of landmarks in human navigation has long been recognized in multiple fields. These include areas involved in the understanding, modelling and supporting wayfinding, spatial knowledge acquisition, and place recognition. From the Psychological, Linguistic and Cognitive Neuroscience viewpoint, the perceived landmarkness of discrete objects vary among individuals. Thus, the key challenge lies in identifying those properties, which remain relevant across a wide range of individual differences, experiences, and behavioural patterns. From the Computer Science, Artificial Intelligence and Cognitive Modelling perspective, formalising these relations in a manner successfully matching the landmark’s relevance for humans has proven difficult.

Most recently, the increasing volume and accessibility of semantically rich geospatial data has opened new avenues for further progress in this area. The continuing collaboration between these fields is exemplified by the regular conference series on spatial information theory and geospatial science as well as multiple on-going interdisciplinary research projects.

In spite of that, technologies used to support human navigation struggle to incorporate the type of landmark information relevant for the human user. The gap between the human’s and the computer’s understanding of what constitutes a landmark remains one of the major challenges in the development of spatial systems intuitive in use as well as in modelling navigational behaviour similar to this of a human.

This special issue integrates theoretical, experimental and computational contributions from disciplines involved in the study of landmark-based  navigation in cognitive systems.  The aim of the issue is to identify  new areas  for potential interdisciplinary collaboration and we invite applications focusing on, but not limited to, the following topics:

– Automatic, semi-automatic, and crowd-sourced detection of landmarks.

– Modelling of landmark-based navigation.

– Landmark knowledge acquisition and use.

– Communication of landmark-ness.

– Landmark-based approaches for indoor navigation.

– Human-computer interaction with landmark-based systems.

– Ubiquitous computing applications of the landmark concept.