| EPSRC Reference: |
GR/S63274/01 |
| Title: |
Research Cluster in Swarm intelligence |
| Principal Investigator: |
Freitas, Professor A |
| Other Investigators: |
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| Project Partners: |
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| Department: |
Sch of Computing |
| Organisation: |
University of Kent |
| Scheme: |
Standard Research (Pre-FEC) |
| Starts: |
01 July 2003 |
Ends: |
31 December 2003 |
Value (£): |
39,561
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| EPSRC Research Topic Classifications: |
| Artificial Intelligence |
Biomedical neuroscience |
| Mathematical Aspects of OR |
New & Emerging Comp. Paradigms |
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| EPSRC Industrial Sector Classifications: |
| No relevance to Underpinning Sectors |
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Summary on Grant Application Form |
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Swarm intelligence is a new computational intelligence paradigm combining concepts and principles derived from several fields, such as biology (social insects), mathematics and social sciences. The basic idea underlying swarm intelligence is that individuals following very simple rules interact to produce an intelligent behaviour - often a solution to a complex problem - at the higher level of the society of individuals. Hence, intelligence is an emergent phenomenon, i.e., the whole is more than the sum of the parts .The aim of this proposal is to establish an interdisciplinary research cluster involving computer scientists, biologists and mathematicians to develop high-quality research proposals in the rapidly developing area of swarm intelligence.There is a lot to be gained from closer interactions between scientists with different backgrounds. These interactions can be divided into two broad approaches. On one hand, a computational and/or mathematical model of a swarm intelligence phenomenon can shed light onto that phenomenon. In particular, computational models allow researchers to exploit several different scenarios - asking what if.... questions - in a relatively short time period (typically, much shorter than the time required to perform the corresponding biological experiment). Mathematical models allow researchers to obtain proofs of convergence and other formal results about the behaviour of the system (either a natural or artificial system). On the other hand, a more accurate understanding of biological swarm intelligence can lead to the design of a more effective computational intelligence algorithm.
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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 |
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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: |
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| Further Information: |
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| Organisation Website: |
http://www.kent.ac.uk |