Journal of the ACM (JACM)
Qualitative representation of positional information
Artificial Intelligence
A new approach to cyclic ordering of 2D orientations using ternary relation algebras
Artificial Intelligence
Using Orientation Information for Qualitative Spatial Reasoning
Proceedings of the International Conference GIS - From Space to Territory: Theories and Methods of Spatio-Temporal Reasoning on Theories and Methods of Spatio-Temporal Reasoning in Geographic Space
Qualitative spatial reasoning about relative point position
Journal of Visual Languages and Computing
Representing Relative Direction as a Binary Relation of Oriented Points
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
On generalizing orientation information in OPRAm
KI'06 Proceedings of the 29th annual German conference on Artificial intelligence
Qualitative reasoning with directional relations
Artificial Intelligence
SC'04 Proceedings of the 4th international conference on Spatial Cognition: reasoning, Action, Interaction
Exploiting qualitative spatial neighborhoods in the situation calculus
SC'04 Proceedings of the 4th international conference on Spatial Cognition: reasoning, Action, Interaction
A condensed semantics for qualitative spatial reasoning about oriented straight line segments
Artificial Intelligence
StarVars: effective reasoning about relative directions
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Multi-granularity and metric spatial reasoning
Expert Systems with Applications: An International Journal
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An important issue in qualitative spatial reasoning is the representation of relative directions. In this paper we present simple geometric rules that enable reasoning about the relative direction between oriented points. This framework, the oriented point algebra OPRA"m, has a scalable granularity m. We develop a simple algorithm for computing the OPRA"m composition tables and prove its correctness. Using a composition table, algebraic closure for a set of OPRA"m statements is very useful for solving spatial navigation tasks. It turns out that scalable granularity is useful in these navigation tasks.