| EPSRC Reference: |
GR/S20727/01 |
| Title: |
DARP: Data Fusion DARP: ARGUS II |
| Principal Investigator: |
Jennings, Professor N |
| Other Investigators: |
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| Researcher Co-Investigators: |
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| Project Partners: |
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| 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
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| EPSRC Research Topic Classifications: |
| Information & Knowledge Mgmt |
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| EPSRC Industrial Sector Classifications: |
| Aerospace, Defence and Marine |
Communications |
| Electronics |
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| Related Grants: |
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| Panel History: |
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Summary on Grant Application Form |
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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.
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Key Findings |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Potential use in non-academic contexts |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Impacts |
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| Description |
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk |
| Summary |
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| Date Materialised |
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Sectors submitted by the Researcher |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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| Project URL: |
http://www.robots.ox.ac.uk/~argus/ |
| Further Information: |
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| Organisation Website: |
http://www.soton.ac.uk |