Name
Abstract | ||
|---|---|---|
We discuss the problems of spatio-temporal reasoning in the context of hierarchical information maps and approximate reasoning networks (AR networks). Hierarchical information maps are used for representations of domain knowledge about objects, their parts, and their dynamical changes. AR networks are patterns constructed over sensory measurements and they are discovered from hierarchical information maps and experimental data. They make it possible to approximate domain knowledge, i.e., complex spatio-temporal concepts and reasonings represented in hierarchical information maps. Experiments with classifiers based on AR schemes using a road traffic simulator are also briefly presented. |
Year | Venue | Keywords |
|---|---|---|
2005 | Fundam. Inform. | spatio-temporal approximate reasoning,approximate reasoning network,ar network,complex objects,dynamical change,domain knowledge,hierarchical information map,experimental data,approximate domain knowledge,spatio-temporal reasoning,complex spatio-temporal concept,ar scheme,pattern |
Field | DocType | Volume |
Domain knowledge,Experimental data,Computer science,Road traffic,Theoretical computer science,Approximate reasoning,Artificial intelligence | Journal | 67 |
Issue | ISSN | Citations |
1-3 | 0169-2968 | 11 |
PageRank | References | Authors |
0.55 | 15 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Piotr Synak | 1 | 481 | 45.13 |
| Jan G. Bazan | 2 | 11 | 0.55 |
| Andrzej Skowron | 3 | 5062 | 421.31 |
| James F. Peters | 4 | 1825 | 184.11 |