Name
Abstract | ||
|---|---|---|
Several problems are connected, in the literature, to causality: prediction, explan ation, action, planning and natural language processing.... In a recent paper, Halpern and Pearl introduced an elegant definition of causal (partial) explanation in the structural-model approach, which is based on their notions of weak and actual cause [5]. Our purpose in this paper is to partially modify this definition, rather than to use a probability (quantitative modelisation) we suggest to affect a degree of possibility (a more qualitative modelisation) which is nearer to the human way of reasoning, by using the possibilistic logic. A stratification of all possible partial explanations will be given to the agent for a given request, the explanations in the first strate are more possible than those belonging to the other strates. We compute the complexity of this strafication. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/3-540-34777-1_35 | SOFT METHODS FOR INTEGRATED UNCERTAINTY MODELLING |
Keywords | Field | DocType |
natural language processing,stratification | Stratification (seeds),Computer science,Artificial intelligence,Possibility distribution,Machine learning | Conference |
ISSN | Citations | PageRank |
1615-3871 | 0 | 0.34 |
References | Authors | |
3 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sara Boutouhami | 1 | 1 | 1.82 |
| Aïcha Mokhtari | 2 | 46 | 11.97 |