Just-In-Time Constraint-Based Inference for Qualitative Spatial and Temporal Reasoning
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DISCUSSION
Just‑In‑Time Constraint‑Based Inference for Qualitative Spatial and Temporal Reasoning Michael Sioutis1 Received: 16 August 2019 / Accepted: 19 March 2020 © Gesellschaft für Informatik e.V. and Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract We discuss a research roadmap for going beyond the state of the art in qualitative spatial and temporal reasoning (QSTR). Simply put, QSTR is a major field of study in Artificial Intelligence that abstracts from numerical quantities of space and time by using qualitative descriptions instead (e.g., precedes, contains, is left of); thus, it provides a concise framework that allows for rather inexpensive reasoning about entities located in space or time. Applications of QSTR can be found in a plethora of areas and domains such as smart environments, intelligent vehicles, and unmanned aircraft systems. Our discussion involves researching novel local consistencies in the aforementioned discipline, defining dynamic algorithms pertaining to these consistencies that can allow for efficient reasoning over changing spatio-temporal information, and leveraging the structures of the locally consistent related problems with regard to novel decomposability and theoretical tractability properties. Ultimately, we argue for pushing the envelope in QSTR via defining tools for tackling dynamic variants of the fundamental reasoning problems in this discipline, i.e., problems stated in terms of changing input data. Indeed, time is a continuous flow and spatial objects can change (e.g., in shape, size, or structure) as time passes; therefore, it is pertinent to be able to efficiently reason about dynamic spatio-temporal data. Finally, these tools are to be integrated into the larger context of highly active areas such as neuro-symbolic learning and reasoning, planning, data mining, and robotic applications. Our final goal is to inspire further discussion in the community about constraint-based QSTR in general, and the possible lines of future research that we outline here in particular. Keywords Qualitative constraints · Spatio-temporal reasoning · Just-in-time inference · Local consistencies · Singleton checks · Dynamic algorithms · Decomposability · Adaptivity · Parallelization
1 Background and Motivation Qualitative Spatial and Temporal Reasoning (QSTR) is a major field of study in Artificial Intelligence, and in particular in Knowledge Representation and Reasoning, that deals with the fundamental cognitive concepts of space and time in an abstract, qualitative, manner, and ranges from theoretical computer science, mathematics, and logic to practical algorithms and applications [58]. In a sense, this approach is Taking place at the time when it is needed, at run time, and not in advance. * Michael Sioutis michail.sioutis@uni‑bamberg.de 1
Faculty of Information Systems and Applied Computer Sciences, University of Bamberg, An der Weberei 5, 96047 Bamberg, Germany
in line with the qualitative abstractions of spatial and temporal aspects of the common-sense bac
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