Name
Abstract | ||
|---|---|---|
Most AI representations and algorithms for plan generationhave not included the concept of informationproducingactions (also called diagnostics, or tests,in the decision making literature). We present aplanning representation and algorithm that modelsinformation-producing actions and constructs plansthat exploit the information produced by those actions.We extend the buridan (Kushmerick et al.1994) probabilistic planning algorithm, adapting theaction representation to model the... |
Year | Venue | Field |
|---|---|---|
1994 | AIPS | Imperfect,Planning algorithms,Computer science,Planner,Exploit,Artificial intelligence,Probabilistic logic,Machine learning,Conditional execution |
DocType | Citations | PageRank |
Conference | 104 | 17.03 |
References | Authors | |
5 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Denise Draper | 1 | 117 | 18.02 |
| Steve Hanks | 2 | 623 | 151.36 |
| Daniel S. Weld | 3 | 10298 | 1127.49 |