Just-In-Time Constraint-Based Inference for Qualitative Spatial and Temporal Reasoning

Faculty/Professorship: Smart Environments  
Author(s): Sioutis, Michael
Publisher Information: Bamberg : Otto-Friedrich-Universität
Year of publication: 2022
Pages: 259-270
Source/Other editions: KI - Künstliche Intelligenz, 34 (2020), 2 , S. 259-270 - ISSN: 1610-1987
is version of: 10.1007/s13218-020-00652-z
Year of first publication: 2020
Language(s): English
Licence: Creative Commons - CC BY - Attribution 4.0 International 
URN: urn:nbn:de:bvb:473-irb-551845
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.
GND Keywords: Constraint <Künstliche Intelligenz>; Räumliches Schließen; Temporales Schließen; Folgerung; Just-in-time-Prinzip; Konsistenz <Informatik>; Entwurfsmuster; Algorithmus; Adaptives System; Parallelisierung
Keywords: Qualitative constraints, Spatio-temporal reasoning, Just-in-time inference, Local consistencies, Singleton checks, Dynamic algorithms, Decomposability, Adaptivity, Parallelization
DDC Classification: 004 Computer science  
RVK Classification: ST 300   
Type: Article
URI: https://fis.uni-bamberg.de/handle/uniba/55184
Release Date: 12. September 2022

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