Stream-Based Hierarchical Anchoring
| dc.contributor.author | Heintz, Fredrik | |
| dc.contributor.author | Kvarnström, Jonas | |
| dc.contributor.author | Doherty, Patrick | |
| dc.date.accessioned | 2018-01-08T09:16:32Z | |
| dc.date.available | 2018-01-08T09:16:32Z | |
| dc.date.issued | 2013 | |
| dc.description.abstract | Autonomous systems situated in the real world often need to recognize, track, and reason about various types of physical objects. In order to allow reasoning at a symbolic level, one must create and continuously maintain a correlation between symbols denoting physical objects and sensor data being collected about them, a process called anchoring.In this paper we present a stream-based hierarchical anchoring framework. A classification hierarchy is associated with expressive conditions for hypothesizing the type and identity of an object given streams of temporally tagged sensor data. The anchoring process constructs and maintains a set of object linkage structures representing the best possible hypotheses at any time. Each hypothesis can be incrementally generalized or narrowed down as new sensor data arrives. Symbols can be associated with an object at any level of classification, permitting symbolic reasoning on different levels of abstraction. The approach is integrated in the DyKnow knowledge processing middleware and has been applied to an unmanned aerial vehicle traffic monitoring application. | |
| dc.identifier.pissn | 1610-1987 | |
| dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/11346 | |
| dc.publisher | Springer | |
| dc.relation.ispartof | KI - Künstliche Intelligenz: Vol. 27, No. 2 | |
| dc.relation.ispartofseries | KI - Künstliche Intelligenz | |
| dc.title | Stream-Based Hierarchical Anchoring | |
| dc.type | Text/Journal Article | |
| gi.citation.endPage | 128 | |
| gi.citation.startPage | 119 |