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Hsiang-Lung Wu

dblp:21/3699 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 1990
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 first-authorGraphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Robot navigation and mapping · 44% Probabilistic and Bayesian machine learning · 44% Multi-agent systems · 13%

Topics — the 2 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Probabilistic and Bayesian machine learning
bayesian decision theory		 0.011990
A Bayesian decision theoretic approach for adaptive goal-directed sensing · ICCV 1990
Knowledge, reasoning and agents › Multi-agent systems
multi-sensor systems		 0.011990
A Bayesian decision theoretic approach for adaptive goal-directed sensing · ICCV 1990

Methods — techniques the papers use, named apart from their topics

bayesian decision theory · 0.0
YearPublicationVenuePosition
1990 A Bayesian decision theoretic approach for adaptive goal-directed sensing
abstract
A general mathematical framework is described for applying Bayesian decision theory to selecting optimal sensing actions for achieving a given sensory goal. The information utilized in the selection process for achieving the goal includes all sensory data acquired prior to the currently considered action. This enables the selection of intelligent sensing strategies to be both adaptive and goal-directed. The authors first show how Bayesian decision theory can facilitate the selection of plans for collecting information relevant to a given task. The approach taken is quite general. It is directly applicable to multi-sensor systems. It could be used in selecting sensory actions to acquire multiple types of information at once. The use of the approach is demonstrated with an example from the domain of robot vision.>
Hsiang-Lung Wu, Alec Cameron
 ICCV1