Difference between revisions of "Trust"

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<LI>[http://knoesis.wright.edu/research/semsci/application_domain/sem_sensor/projects/trusted_ssw/ont/trustontology.owl Trust Ontology]</LI>
 
<LI>[http://knoesis.wright.edu/research/semsci/application_domain/sem_sensor/projects/trusted_ssw/ont/trustontology.owl Trust Ontology]</LI>

Revision as of 19:43, 15 November 2010

Trust in Semantic Social and Sensor Networks

Millions of sensors around the globe currently collect avalanches of data about our environment. The rapid development and deployment of sensor technology involves many different types of sensors, both remote and in situ, with such diverse capabilities as range, modality, and maneuverability. It is possible today to utilize networks with multiple sensors to detect and identify objects of interest up close or from a great distance. Similarly, millions of people are reporting their observations, opinions and perceptions of their environment. We see trust to play a crucial role in both sensor and social networks as the consumers of this data becomes more and more separated from the producers.

Research Topics

Trust on Semantic Sensor Web

Trust and confidence are becoming key issues in diverse applications such as ecommerce, social networks, semantic sensor web, semantic web information retrieval systems, etc. Both humans and machines use some form of trust to make informed and reliable decisions before acting. In this work, we briefly review existing work on trust networks, pointing out some of its drawbacks. We then propose a local framework to explore two different kinds of trust among agents called referral trust and functional trust, that are modelled using local partial orders, to enable qualitative trust personalization. The proposed approach formalizes reasoning with trust, distinguishing between direct and inferred trust. It is also capable of dealing with general trust networks with cycles.

Perception and Analysis of Sensor Data

Currently, there are many sensors collecting information about our environment, leading to an overwhelming number of observations that must be analyzed and explained in order to achieve situation awareness. As perceptual beings, we are also constantly inundated with sensory data; yet we are able to make sense out of our environment with relative ease. This is due, in part, to the bi-directional information flow between our sensory organs and analytical brain. Drawing inspiration from cognitive models of perception, we can improve machine perception by allowing communication from processes that analyze observations to processes that generate observations. Such a perceptual system provides effective utilization of resources by decreasing the cost and number of observations needed for achieving situation awareness.

People


Publications


Presentations


Ontologies


Prototypes, Demos and Tools

  • Trusted Perception Cycle: demo


Related Material

Computing for Human Experience


Contact Information: Pramod Anantharam