Name
Abstract | ||
|---|---|---|
The Dempster–Shafer belief structure provides a representation of a variable in which our knowledge of its probability distribution is imprecise. Here compatibility relations, which encode relationships between variables, enable inference about a consequent variable using knowledge about the input variable. Here we extend the capability of these compatibility relations to enable the representation of nonmonotonic relations, such as default rules. This allows situations in which an increase in information about the input variable can result in a decrease in information about the secondary variable. We show what are the conditions required of a compatibility relation to lead to monotonic and nonmonotonic inferences. We provide some examples of nonmonotonic relations. |
Year | DOI | Venue |
|---|---|---|
2002 | 10.1023/A:1014430023897 | Ann. Math. Artif. Intell. |
Keywords | Field | DocType |
Neural Network,Probability Distribution,Artificial Intelligence,Complex System,Nonlinear Dynamics | Discrete mathematics,Monotonic function,Nonlinear system,Compatibility (mechanics),Inference,Belief structure,Probability distribution,Artificial intelligence,Artificial neural network,Mathematics,Machine learning | Journal |
Volume | Issue | ISSN |
34 | 1-3 | 1573-7470 |
Citations | PageRank | References |
0 | 0.34 | 4 |
Authors | ||
1 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ronald R. Yager | 1 | 986 | 206.03 |