Qualitative representation of positional information
Artificial Intelligence
Combining topological and size information for spatial reasoning
Artificial Intelligence
Symbolic representation of user-defined time granularities
Annals of Mathematics and Artificial Intelligence
Calendars, Time Granularities, and Automata
SSTD '01 Proceedings of the 7th International Symposium on Advances in Spatial and Temporal Databases
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
COSIT'11 Proceedings of the 10th international conference on Spatial information theory
Solving qualitative constraints involving landmarks
CP'11 Proceedings of the 17th international conference on Principles and practice of constraint programming
Qualitative reasoning about relative direction of oriented points
Artificial Intelligence
Multi-agent location system in wireless networks
Expert Systems with Applications: An International Journal
Qualitative distances and qualitative image descriptions for representing indoor scenes in robotics
Pattern Recognition Letters
QRPC: A new qualitative model for representing motion patterns
Expert Systems with Applications: An International Journal
| Hi-index | 12.05 |
Composition reasoning is a basic reasoning task in qualitative spatial reasoning (QSR). It is an important qualitative method for robot navigation, node localization in wireless sensor networks and other fields. The previous composition reasoning works dedicated in single granularity framework. Multi-granularity spatial relation is not rare in real world, and some qualitative spatial relation models are multi-granularity models, such as RCC, STAR"m, CDC"m and OPRA"m. Although multi-granularity composition reasoning is very useful in many applications, it has not been systematically studied before. A special case of multi-granularity composition reasoning, referred to as metric spatial reasoning, is also discussed here. The general frameworks and basic theories for multi-granularity and metric spatial reasoning are put forward here. Furthermore, we redefine the spatial relation models for distance, topology and direction under the proposed multi-granularity and metric frameworks. We add metric representation for the OPRA"m. The multi-granularity and metric reasoning tasks are studied for these four models for the first time. Finally we perform some experiments on OPRA"m with encouraging results to verify our theories. Multi-granularity and metric spatial reasoning tasks are new problems in QSR and quite different from the previous works. Our works can be potentially applied in robot navigation, wireless sensor networks and other applications.