Name
Abstract | ||
|---|---|---|
This article discusses the quadratization of Markov Logic Networks, which enables efficient approximate MAP computation by means of maximum flows. The procedure relies on a pseudo-Boolean representation of the model, and allows handling models of any order. The employed pseudo-Boolean representation can be used to identify problems that are guaranteed to be solvable in low polynomial-time. Results on common benchmark problems show that the proposed approach finds optimal assignments for most variables in excellent computational time and approximate solutions that match the quality of ILP-based solvers. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1613/jair.5023 | JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH |
Field | DocType | Volume |
Mathematical optimization,Markov chain,Duality (optimization),Artificial intelligence,Roof,Mathematics,Machine learning,Computation | Journal | 55 |
Issue | ISSN | Citations |
1 | 1076-9757 | 0 |
PageRank | References | Authors |
0.34 | 26 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Roderick De Nijs | 1 | 83 | 5.74 |
| Christian Landsiedel | 2 | 85 | 7.20 |
| Dirk Wollherr | 3 | 673 | 60.01 |
| Martin Buss | 4 | 1799 | 159.02 |