Convergent Approximate Solving of First-Order Constraints by Approximate Quantifiers
| dc.creator | Ratschan, Stefan | |
| dc.date | 2001-08-22 | |
| dc.date | 2002-12-20 | |
| dc.date.accessioned | 2026-07-07T03:17:25Z | |
| dc.date.available | 2026-07-07T03:17:25Z | |
| dc.description | Exactly solving first-order constraints (i.e., first-order formulas over a certain predefined structure) can be a very hard, or even undecidable problem. In continuous structures like the real numbers it is promising to compute approximate solutions instead of exact ones. However, the quantifiers of the first-order predicate language are an obstacle to allowing approximations to arbitrary small error bounds. In this paper we solve the problem by modifying the first-order language and replacing the classical quantifiers with approximate quantifiers. These also have two additional advantages: First, they are tunable, in the sense that they allow the user to decide on the trade-off between precision and efficiency. Second, they introduce additional expressivity into the first-order language by allowing reasoning over the size of solution sets. | |
| dc.identifier | https://arxiv.org/abs/cs/0108013 | |
| dc.identifier | http://arxiv.org/abs/cs/0108013 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/30717 | |
| dc.subject | Logic in Computer Science | |
| dc.subject | Artificial Intelligence | |
| dc.subject | F.4.1; I.2.3 | |
| dc.title | Convergent Approximate Solving of First-Order Constraints by Approximate Quantifiers | |
| dc.type | text |