Name
Title | ||
|---|---|---|
Bayesphone: precomputation of context-sensitive policies for inquiry and action in mobile devices |
Abstract | ||
|---|---|---|
Inference and decision making with probabilistic user models may be infeasible on portable devices such as cell phones. We highlight the opportunity for storing and using precomputed inferences about ideal actions for future situations, based on offline learning and reasoning with the user models. As a motivating example, we focus on the use precomputation of call-handling policies for cell phones. The methods hinge on the learning of Bayesian user models for predicting whether users will attend meetings on their calendar and the cost of being interrupted by incoming calls should a meeting be attended. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1007/11527886_33 | User Modeling |
Keywords | Field | DocType |
incoming call,bayesian user model,portable device,mobile device,offline learning,ideal action,call-handling policy,cell phone,future situation,context-sensitive policy,user model,probabilistic user model | Mobile computing,Offline learning,Precomputation,Inference,Computer security,Computer science,Decision support system,Human–computer interaction,Mobile device,Probabilistic logic,Mobile phone | Conference |
Volume | ISSN | ISBN |
3538 | 0302-9743 | 3-540-27885-0 |
Citations | PageRank | References |
34 | 2.58 | 5 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Eric Horvitz | 1 | 9402 | 1058.25 |
| Paul Koch | 2 | 309 | 20.55 |
| Raman Sarin | 3 | 778 | 68.77 |
| Johnson Apacible | 4 | 469 | 29.62 |
| Muru Subramani | 5 | 74 | 5.60 |