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Details of Grant 

EPSRC Reference: GR/S20727/01
Title: DARP: Data Fusion DARP: ARGUS II
Principal Investigator: Jennings, Professor N
Other Investigators:
Rogers, Professor AC
Researcher Co-Investigators:
Project Partners:
BAE Systems QinetiQ Rolls-Royce Plc
Department: Electronics and Computer Science
Organisation: University of Southampton
Scheme: Standard Research (Pre-FEC)
Starts: 01 April 2003 Ends: 31 May 2008 Value (£): 436,234
EPSRC Research Topic Classifications:
Information & Knowledge Mgmt
EPSRC Industrial Sector Classifications:
Electronics Aerospace, Defence and Marine
Communications
Related Grants:
Panel History:  
Summary on Grant Application Form
The central objective of this proposed programme is to develop systems which are able, in a dynamic and uncertain world, to make the best use of available data and information through the management and timely fusion of instantaneous data, historical information and expert knowledge in a distributed manner. The study intends to achieve this goal by modelling a global information processing system as a set of intelligent agents that pass information between themselves in an information economy. An economic metaphor is appropriate since marketplaces are efficient mechanisms for allocating scarce resources in a decentralised fashion. Here, the traded goods are information of varying certainty and the resources are sensors and processing capability. To ensure that these agents handle any uncertainty in a consistent manner (i.e., one of the key issues that real data processing applications face), the exchanged information will be related to probability density measures (in terms of sufficient statistics). The issue of how a consistent and meaningful global representation can be obtained from multiple agent interactions and negotiations operating on disparate data sources is one of the key issues that will be addressed in this study. Specifically, it is proposed to undertake the information fusion between multiple agents, current data streams, and historical and subjective data by using a Bayesian graphical model paradigm. The approaches developed will be assessed on real industrial data sets.
Key Findings
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Summary
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Project URL: http://www.robots.ox.ac.uk/~argus/
Further Information:  
Organisation Website: http://www.soton.ac.uk