Name
Abstract | ||
|---|---|---|
This paper presents the search problem formu- lated as a decision problem, where the searcher decides whether the target is present in the search region, and if so, where it is located. Such decision-based search tasks are relevant to many research areas, including mobile robot missions, visual search and attention, and event detection in sensor networks. The effect of control strategies in search problems on decision- making quantities, namely time-to-decision, is investigated in this work. We present a Bayesian framework in which the objective is to improve the decision, rather than the sensing, using different control policies. Furthermore, derivations of closed-form expressions governing the evolution of the belief function are also presented. As this framework enables the study and comparison of the role of control for decision-making applications, the derived theoretical results provide greater insight into the sequential processing of decisions. Numerical studies are presented to verify and demonstrate these results. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/ROBOT.2007.364155 | Roma |
Keywords | Field | DocType |
Bayes methods,decision making,search problems,statistical analysis,Bayesian framework,control strategies,decision making,probabilistic search problem | Visual search,Decision problem,Expression (mathematics),Computer science,Automatic control,Artificial intelligence,Probabilistic logic,Search problem,Wireless sensor network,Mobile robot,Machine learning | Conference |
Volume | Issue | ISSN |
2007 | 1 | 1050-4729 E-ISBN : 1-4244-0602-1 |
ISBN | Citations | PageRank |
1-4244-0602-1 | 29 | 1.55 |
References | Authors | |
6 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Timothy H. Chung | 1 | 465 | 36.31 |
| Burdick, J.W. | 2 | 2988 | 516.87 |