Evaluating uncertainty representation and reasoning in HLF systems

High-level fusion of hard and soft information from diverse sensor types still depends heavily on human cognition. This results in a scalability conundrum that current technologies are incapable of solving. Although there is widespread acknowledgement that an HLF framework must support automated kno...

Full description

Saved in:
Bibliographic Details
Main Authors: Costa, P. C. G., Carvalho, R. N., Laskey, K. B., Cheol Young Park
Format: Conference Proceeding
Language:English
Subjects:
Bayesian theory
Cognition
Communities
Dempster-Shafer theory
evaluation framework
fuzzy theory
high-level fusion
Knowledge representation
Marine vehicles
Probabilistic logic
simulation
Testing
Uncertainty
Uncertainty reasoning
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:High-level fusion of hard and soft information from diverse sensor types still depends heavily on human cognition. This results in a scalability conundrum that current technologies are incapable of solving. Although there is widespread acknowledgement that an HLF framework must support automated knowledge representation and reasoning with uncertainty, there is no consensus on the most appropriate technology to satisfy this requirement. Further, the debate among proponents of the various approaches is laden with miscommunication and ill-supported assumptions, which inhibits advancement of HLF research as a whole. A clearly defined, scientifically rigorous evaluation framework is needed to help information fusion researchers assess the suitability of various approaches and tools to their applications. This paper describes requirements for such a framework and describes a use case in HLF evaluation.