Name
Abstract | ||
|---|---|---|
Modus ponens (from A and "if A then C" infer C) is one of the most basic inference rules. The probabilistic modus ponens allows for managing uncertainty by transmitting assigned uncertainties from the premises to the conclusion (i.e., from P(A) and P(C vertical bar A) infer P(C)). In this paper, we generalize the probabilistic modus ponens by replacing A by the conditional event A vertical bar H. The resulting inference rule involves iterated conditionals (formalized by conditional random quantities) and propagates previsions from the premises to the conclusion. Interestingly, the propagation rules for the lower and the upper bounds on the conclusion of the generalized probabilistic modus ponens coincide with the respective bounds on the conclusion for the (non-nested) probabilistic modus ponens. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/978-3-319-61581-3_43 | Lecture Notes in Artificial Intelligence |
Keywords | Field | DocType |
Coherence,Conditional random quantities,Conjoined conditionals,Iterated conditionals,Modus ponens,Prevision | Discrete mathematics,Modus ponens,Computer science,Probabilistic logic,Modus ponendo tollens,Iterated function,Rule of inference,Calculus | Conference |
Volume | ISSN | Citations |
10369 | 0302-9743 | 4 |
PageRank | References | Authors |
0.41 | 13 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Giuseppe Sanfilippo | 1 | 204 | 17.14 |
| Niki Pfeifer | 2 | 74 | 9.14 |
| Angelo Gilio | 3 | 419 | 42.04 |