Name
Abstract | ||
|---|---|---|
We propose an operational framework which builds on the classical understanding of abductive reasoning in logic programming, and extends it in several directions. The new features include the ability to reason with a dynamic knowledge base, where new facts can be added anytime, the ability to generate expectations about such new facts occurring in the future (forecasting), and the process of confirmation/disconfirmation of such expectations. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1007/11558590_14 | AI*IA |
Keywords | Field | DocType |
sciff abductive proof-procedure,classical understanding,operational framework,dynamic knowledge base,logic programming,new fact,abductive reasoning,new feature,knowledge base | Logic program,Computer science,Constraint satisfaction problem,Abductive logic programming,Data integrity,Abductive reasoning,Artificial intelligence,Knowledge base,Logic programming,Proof procedure | Conference |
Volume | ISSN | ISBN |
3673 | 0302-9743 | 3-540-29041-9 |
Citations | PageRank | References |
22 | 0.93 | 18 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| marco alberti | 1 | 467 | 30.93 |
| Marco Gavanelli | 2 | 596 | 48.07 |
| Evelina Lamma | 3 | 1268 | 109.21 |
| Paola Mello | 4 | 444 | 21.33 |
| Paolo Torroni | 5 | 1167 | 80.57 |