Name
Abstract | ||
|---|---|---|
We use imprecise probabilities, based on a concept of generalized coherence, for the management of uncertainty in artificial intelligence. With the aim of reducing the computational difficulties, in the checking of generalized coherence we propose a method which exploits, in the framework of the betting criterion, suitable subsets of the sets of values of the random gains. We give an algorithm in each step of which a linear system with a reduced number of unknowns can be used. Our method improves a procedure already existing in literature and could be integrated with recent approaches of other authors, who exploit suitable logical conditions with the aim of splitting the problem into subproblems. We remark that our approach could be also used in combination with efficient methods like column generation techniques. Finally, to illustrate our method, we give some examples. |
Year | DOI | Venue |
|---|---|---|
2002 | 10.1023/A:1014570831884 | Ann. Math. Artif. Intell. |
Keywords | DocType | Volume |
conditional probability bounds,betting criterion,random gain,alternative theorems,g-coherence checking,not relevant gains,basic sets,algorithms,computational aspects | Journal | 35 |
Issue | ISSN | Citations |
1-4 | 1573-7470 | 14 |
PageRank | References | Authors |
0.96 | 17 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Veronica Biazzo | 1 | 172 | 10.42 |
| Angelo Gilio | 2 | 419 | 42.04 |