PROGNOS: Predictive situational awareness with probabilistic ontologies

Information in the battlefield comes from reports from diverse sources, in distinct syntax, and with different meanings. There are many kinds of uncertainty involved in this process, e.g., noise in sensors, incorrect, incomplete, or deceptive human intelligence, and others, which makes it essential...

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Bibliographic Details
Main Authors: Carvalho, R N, Costa, P C G, Laskey, K B, Chang, K C
Format: Conference Proceeding
Language:English
Subjects:
Algorithm design and analysis
Bayesian methods
Bayesian networks
Cognition
distributed hybrid inference
Inference algorithms
Marine vehicles
MEBN
Ontologies
predictive situational awareness
Probabilistic logic
probabilistic ontologies
probabilistic reasoning
PROWL
spatio-temporal hybrid analysis
web services
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Summary:Information in the battlefield comes from reports from diverse sources, in distinct syntax, and with different meanings. There are many kinds of uncertainty involved in this process, e.g., noise in sensors, incorrect, incomplete, or deceptive human intelligence, and others, which makes it essential to have a coherent, consistent, and principled means to represent such phenomena among the systems performing Predictive Situation Awareness (PSAW). PROGNOS is a PSAW system being developed to work within the operational context such as U.S. Navy's FORCENet. It employs probabilistic ontologies in a distributed system architecture as a means to provide semantic interoperability within an intrinsically complex and uncertain environment. This paper explores our current status in developing the system while addressing the major research challenges for making an effective PSAW system to support maritime operations.
DOI:10.1109/ICIF.2010.5711970