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Details of Grant 

EPSRC Reference: GR/R30792/01
Title: Stochastic Constraint Programming
Principal Investigator: Manandhar, Dr S
Other Investigators:
Researcher Co-Investigators:
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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
EPSRC Research Topic Classifications:
Artificial Intelligence
EPSRC Industrial Sector Classifications:
Financial Services Information Technologies
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Panel History:  
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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Organisation Website: http://www.york.ac.uk