Russell and the foundations of qualitative spatial reasoning: the first steps

2021 ◽  
Vol 46 (4) ◽  
pp. 591-608
Author(s):  
Adam Trybus
Author(s):  
Kazuko Takahashi

This chapter describes a framework called PLCA for Qualitative Spatial Reasoning (QSR) based on the connection patterns of regions. The goal of this chapter is to provide a simple but expressive and feasible representation for qualitative data with sufficient reasoning ability. PLCA provides a symbolic representation for spatial data using simple objects. The authors of this chapter define its expression and operations on it, and show the correspondance between the expression and a figure. PLCA also provides semantical reasoning incorporated with spatial reasoning. Moreover, it can be extended to handle shapes of regions. Throughout the study, the authors discovered many topics that relate QSR to other research areas such as topology, graph theory, and computational geometry, while achieving the research goals. This indicates that QSR is a very fruitful research area.


1997 ◽  
Vol 06 (04) ◽  
pp. 451-480 ◽  
Author(s):  
M. Teresa Escrig ◽  
Francisco Toledo

Human beings reason about different aspects of space (such as relative orientation, cardinal directions, distance, size and shape of objects) quite easily. With the aim of simulating human behavior, several models for these spatial concepts have been developed in the recent years. Cognitive considerations have made these frameworks qualitative, because they seem to deal better with the imprecision that human perception provides. However, an operational model to reason with all these spatial aspects in an integrated way has not been developed, up to now. The first aim of our research work has been the integration of different spatial concepts into the same spatial model which has been accomplished thanks to the definition of an operational model based on Constrain Logic Programming extended with Constraint Handling Rules. Although other aspects of space have been successfully represented by these techniques [2], in this paper we focus our attention in positional information, that is, orientation integrated with distance information. The Constraint Solver developed for managing positional information has a temporal complexity of O(n) 3, where n is the number of spatial landmarks considered in the reasoning process. The second aim of our work is to apply qualitative spatial reasoning to develop a Qualitative Navigation Simulator.


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