EPSRC Reference: 
EP/S033483/2 
Title: 
Algorithms for Computing with Uncertainty: Theory and Experiments 
Principal Investigator: 
Erlebach, Professor TR 
Other Investigators: 

Researcher CoInvestigators: 

Project Partners: 

Department: 
Computer Science 
Organisation: 
Durham, University of 
Scheme: 
Standard Research 
Starts: 
20 September 2021 
Ends: 
31 October 2023 
Value (£): 
191,339

EPSRC Research Topic Classifications: 
Fundamentals of Computing 


EPSRC Industrial Sector Classifications: 
No relevance to Underpinning Sectors 


Related Grants: 

Panel History: 
Panel Date  Panel Name  Outcome 
05 Mar 2019

EPSRC ICT Prioritisation Panel March 2019

Announced


Summary on Grant Application Form 
How much information should we collect before making a decision? This question underlies the research area of computing with explorable uncertainty. For example, assume that we want to build a network connecting a set of branch offices. For any two locations A and B of branch offices, we have an estimate of the cost for building a link between A and B based on the distance between them. The exact cost of building a link between A and B can be determined by further investigations, but these investigations take time and cost money. If we knew the exact link cost for every pair of locations, we could determine the cheapest way of building the network using a known algorithm for the "minimum spanning tree" problem. The approach of first determining for all pairs of locations the exact cost of building a link between them is not efficient, however: It will take a long time to determine all the exact link costs, and the costs for obtaining that information will be significant. It is therefore desirable to find efficient methods for selecting in a clever way the pairs of locations for which the link costs need to be determined, while still achieving the goal of being able to build a cheap network with the information gained. Algorithms for computing with explorable uncertainty solve such problems: They specify a strategy for selecting the pairs of locations for which exact information should be determined until sufficient information has been gained to determine the best possible network to be built. More generally, computing with explorable uncertainty deals with problems where part of the input is uncertain (known only approximately) but can be obtained at a cost using a query operation.
Previous work on computing with uncertainty has focused on the setting where queries are made one by one sequentially (which may take a long time) and where the goal is to make as few queries as possible while ensuring that sufficient information is obtained to solve the problem optimally. This leaves open the question of how the queries should be selected if a number of queries can be made at the same time in parallel, which is realistic in many applications (for example, in the application outlines above, the exact costs of building links between several pairs of branch office locations could be determined in parallel). Another direction that has not yet been sufficiently considered is the setting where the goal is to optimize a combination of the query cost and the cost of the solution determine in the end. The project aims to take research in computing with explorable uncertainty to the next level by addressing these open questions and developing new algorithms that work provably well in the described scenarios. Methods developed in the project can potentially be useful to any decisionmaking scenarios where additional information about the input data of a problem is available in principle and can be obtained at a cost.

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