Qualitative and Semi-Quantitative Inductive Reasoning with Conditionals
| dc.contributor.author | Eichhorn, Christian | |
| dc.contributor.author | Kern-Isberner, Gabriele | |
| dc.date.accessioned | 2018-01-08T09:17:54Z | |
| dc.date.available | 2018-01-08T09:17:54Z | |
| dc.date.issued | 2015 | |
| dc.description.abstract | Conditionals like “birds fly—if bird then fly” are crucial for commonsense reasoning. In this technical project report we show that conditional logics provide a powerful formal framework that helps understanding if-then sentences in a way that is much closer to human reasoning than classical logic and allows for high-quality reasoning methods. We describe methods that inductively generate models from conditional knowledge bases. For this, we use both qualitative (like preferential models) and semi-quantitative (like Spohn’s ranking functions) semantics. We show similarities and differences between the resulting inference relations with respect to formal properties. We further report on two graphical methods on top of the ranking approaches which allow to decompose the models into smaller, more feasible components and allow for local inferences. | |
| dc.identifier.pissn | 1610-1987 | |
| dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/11471 | |
| dc.publisher | Springer | |
| dc.relation.ispartof | KI - Künstliche Intelligenz: Vol. 29, No. 3 | |
| dc.relation.ispartofseries | KI - Künstliche Intelligenz | |
| dc.subject | Conditional structures | |
| dc.subject | Conditionals | |
| dc.subject | Induction | |
| dc.subject | Networks | |
| dc.subject | Nonmonotonic reasoning | |
| dc.subject | Ordinal conditional function | |
| dc.subject | System P | |
| dc.title | Qualitative and Semi-Quantitative Inductive Reasoning with Conditionals | |
| dc.type | Text/Journal Article | |
| gi.citation.endPage | 289 | |
| gi.citation.startPage | 279 |