Theory of linear and integer programming
Theory of linear and integer programming
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
Qualitative Reasoning about Convex Relations
Proceedings of the international conference on Spatial Cognition VI: Learning, Reasoning, and Talking about Space
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
Qualitative reasoning with directional relations
Artificial Intelligence
Qualitative reasoning about relative direction of oriented points
Artificial Intelligence
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Relative direction information is very commonly used. Observers typically describe their environment by specifying the relative directions in which they see other objects or other people from their point of view. Or they receive navigation instructions with respect to their point of view, for example, turn left at the next intersection. However, it is surprisingly hard to integrate relative direction information obtained from different observers, and to reconstruct a model of the environment or the locations of the observers based on this information. Despite intensive research, there is currently no algorithm that can effectively integrate this information: this problem is NP-hard, but not known to be in NP, even if we only use left and right relations. In this paper we present a novel qualitative representation, StarVars, that can solve these problems. It is an extension of the STAR calculus [Renz and Mitra, 2004]) by a VARiable interpretation of the orientation of observers. We show that reasoning in StarVars is in NP and present the first algorithm that allows us to effectively integrate relative direction information from different observers.