Name
Abstract | ||
|---|---|---|
Causality is a non-obvious concept that is often considered to be related to
temporality. In this paper we present a number of past and present approaches
to the definition of temporality and causality from philosophical, physical,
and computational points of view. We note that time is an important ingredient
in many relationships and phenomena. The topic is then divided into the two
main areas of temporal discovery, which is concerned with finding relations
that are stretched over time, and causal discovery, where a claim is made as to
the causal influence of certain events on others. We present a number of
computational tools used for attempting to automatically discover temporal and
causal relations in data. |
Year | Venue | Keywords |
|---|---|---|
2010 | Clinical Orthopaedics and Related Research | artificial intelligent |
Field | DocType | Volume |
Causality,Causal relations,Cognitive science,Artificial intelligence,Machine learning,Mathematics,Temporality | Journal | abs/1007.2 |
Citations | PageRank | References |
0 | 0.34 | 12 |
Authors | ||
1 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kamran Karimi | 1 | 118 | 17.23 |