The CoRg Project: Cognitive Reasoning
| dc.contributor.author | Schon, Claudia | |
| dc.contributor.author | Siebert, Sophie | |
| dc.contributor.author | Stolzenburg, Frieder | |
| dc.date.accessioned | 2021-04-23T09:27:09Z | |
| dc.date.available | 2021-04-23T09:27:09Z | |
| dc.date.issued | 2019 | |
| dc.description.abstract | The term cognitive computing refers to new hardware and/or software that mimics the functioning of the human brain. In the context of question answering and commonsense reasoning this means that the reasoning process of humans shall be modeled by adequate technical means. However, since humans do not follow the rules of classical logic, a system designed to model these abilities must be very versatile. The aim of the CoRg project (Cognitive Reasoning) is to successfully complete a reasoning task with commonsense reasoning. We address different benchmarks with focus on the COPA benchmark set (Choice of Plausible Alternatives). Since humans naturally use background knowledge, we have to deal with large background knowledge bases and must be able to reason with multiple input formats and sources in the CoRg system, in order to draw explainable conclusions. For this, we have to find appropriate logics for cognitive reasoning. For a successful reasoning system, nowadays it seems to be important to combine automated reasoning with machine learning technology like recurrent neural networks. | de |
| dc.identifier.doi | 10.1007/s13218-019-00601-5 | |
| dc.identifier.pissn | 1610-1987 | |
| dc.identifier.uri | http://dx.doi.org/10.1007/s13218-019-00601-5 | |
| dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/36248 | |
| dc.publisher | Springer | |
| dc.relation.ispartof | KI - Künstliche Intelligenz: Vol. 33, No. 3 | |
| dc.relation.ispartofseries | KI - Künstliche Intelligenz | |
| dc.subject | Automated reasoning | |
| dc.subject | Cognitive reasoning | |
| dc.subject | Commonsense reasoning | |
| dc.subject | Machine learning | |
| dc.title | The CoRg Project: Cognitive Reasoning | de |
| dc.type | Text/Journal Article | |
| gi.citation.endPage | 299 | |
| gi.citation.startPage | 293 |