EPSRC Reference: |
GR/R30792/01 |
Title: |
Stochastic Constraint Programming |
Principal Investigator: |
Manandhar, Dr S |
Other Investigators: |
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Researcher Co-Investigators: |
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Project Partners: |
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Department: |
Computer Science |
Organisation: |
University of York |
Scheme: |
Standard Research (Pre-FEC) |
Starts: |
01 January 2002 |
Ends: |
31 December 2003 |
Value (£): |
104,945
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EPSRC Research Topic Classifications: |
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EPSRC Industrial Sector Classifications: |
Financial Services |
Information Technologies |
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Related Grants: |
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Panel History: |
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Summary on Grant Application Form |
Many real world decision problems contain uncertainty. For exampe, data about events in the past may not be known exactly due to errors in measuring or difficulties in sampling, whilst data about events in the future may simply not be known with certainty. To deal with such situations, we propose an extension of constraint programming called stochastic constraint programming . In this framework, we distinguish between decision variables, which we are free to set, and stochastic (or observed) variables, which follow some (conditional) probability distribution. The aims of this project are to develop the formal foundations of stochastic constraint programming and develop methods for solving stochastic constraint programs. We expect such methods to become a vital component of the next generation of constraint programming toolkits. Such toolkits have been used to tackle a wide variety of combinatorial decision problems like resource allocation, transportation and scheduling. The ability to deal with uncertainity in models of such problems will greatly increase the usefulness of these constraint programming toolkits for a wide range of different industries.
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Key Findings |
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Potential use in non-academic contexts |
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Impacts |
Description |
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Summary |
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Date Materialised |
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Sectors submitted by the Researcher |
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Project URL: |
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Further Information: |
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Organisation Website: |
http://www.york.ac.uk |