Name
Abstract | ||
|---|---|---|
Constraint programming is used for a variety of real-world optimization problems, such as planning, scheduling, and resource allocation problems, all while we continuously gather vast amounts of data about these problems. Current constraint programming software doesn't exploit such data to update schedules, resources, and plans. The authors propose a new framework that they call the inductive cons... |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/MIS.2017.3711637 | IEEE Intelligent Systems |
Keywords | Field | DocType |
Artificial intelligence,Programming,Loss measurement,Data mining,Machine learning,Intelligent systems,Constraint optimization | Procedural programming,Functional reactive programming,Data mining,Constraint satisfaction,Programming paradigm,Computer science,Inductive programming,Constraint programming,Concurrent constraint logic programming,Reactive programming | Journal |
Volume | Issue | ISSN |
32 | 5 | 1541-1672 |
Citations | PageRank | References |
1 | 0.37 | 10 |
Authors | ||
10 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Christian Bessière | 1 | 1520 | 114.38 |
| Luc De Raedt | 2 | 5481 | 505.49 |
| Tias Guns | 3 | 332 | 23.14 |
| Lars Kotthoff | 4 | 229 | 24.12 |
| Mirco Nanni | 5 | 1412 | 84.47 |
| Siegfried Nijssen | 6 | 1103 | 59.13 |
| barry osullivan | 7 | 74 | 17.27 |
| Anastasia Paparrizou | 8 | 38 | 9.15 |
| Dino Pedreschi | 9 | 3083 | 244.47 |
| Helmut Simonis | 10 | 908 | 122.73 |