Open-EASE: A Cloud-Based Knowledge Service for Autonomous Learning
| dc.contributor.author | Tenorth, Moritz | |
| dc.contributor.author | Winkler, Jan | |
| dc.contributor.author | Beßler, Daniel | |
| dc.contributor.author | Beetz, Michael | |
| dc.date.accessioned | 2018-01-08T09:18:05Z | |
| dc.date.available | 2018-01-08T09:18:05Z | |
| dc.date.issued | 2015 | |
| dc.description.abstract | We present Open-EASE, a cloud-based knowledge base of robot experience data that can serve as episodic memory, providing a robot with comprehensive information for autonomously learning manipulation tasks. Open-EASE combines both robot and human activity data in a common, semantically annotated knowledge base, including robot poses, object information, environment models, the robot’s intentions and beliefs, as well as information about the actions that have been performed. A powerful query language and inference tools support reasoning about the data and retrieving information based on semantic queries. In this paper, we focus on applications of Open-EASE in the context of autonomous learning. | |
| dc.identifier.pissn | 1610-1987 | |
| dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/11493 | |
| dc.publisher | Springer | |
| dc.relation.ispartof | KI - Künstliche Intelligenz: Vol. 29, No. 4 | |
| dc.relation.ispartofseries | KI - Künstliche Intelligenz | |
| dc.title | Open-EASE: A Cloud-Based Knowledge Service for Autonomous Learning | |
| dc.type | Text/Journal Article | |
| gi.citation.endPage | 411 | |
| gi.citation.startPage | 407 |