Name
Abstract | ||
|---|---|---|
We present a new abductive, probabilistic theory of plan recognition. This model dif- fers from previous theories in being centered around a model of plan execution: most previous methods have been based on plans as formal objects or on rules describing the recognition process. We show that our new model accounts for phenomena omitted from most previous plan recognition theories: no- tably the cumulative effect of a sequence of observations of partially-ordered, interleaved plans and the effect of context on plan adop- tion. The model also supports inferences about the evolution of plan execution in situ- ations where another agent intervenes in plan execution. This facility provides support for using plan recognition to build systems that will intelligently assist a user. |
Year | Venue | Keywords |
|---|---|---|
2013 | UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence | plan adoption,plan recognition,previous plan recognition theory,interleaved plan,plan execution,cumulative effect,previous theory,previous method,recognition process,new model account,partial order,cumulant |
Field | DocType | Volume |
Computer science,Artificial intelligence,Plan recognition,Probabilistic logic,Machine learning | Journal | abs/1301.6700 |
ISBN | Citations | PageRank |
1-55860-614-9 | 59 | 4.80 |
References | Authors | |
7 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Robert Goldman | 1 | 950 | 151.72 |
| Christopher W. Geib | 2 | 321 | 38.82 |
| Christopher A. Miller | 3 | 334 | 46.70 |