Algorithms for Computing with Uncertainty - Theory and Experiments

ACUTE (Algorithms for Computing with Uncertainty - Theory and Experiments) is an EPSRC-funded project that will investigate algorithms for computing with uncertainty.

Investigators

Post-Doc

Visiting Researchers and Collaborators

Summary

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 decision-making scenarios where additional information about the input data of a problem is available in principle and can be obtained at a cost.

Funder

ACUTE is funded by EPSRC (EP/S033483/1).

Publications

  • Christoph Dürr, Thomas Erlebach, Nicole Megow and Julie Meißner:
    An Adversarial Model for Scheduling with Testing
    Algorithmica. Available online since 10 July 2020.
    http://doi.org/10.1007/s00453-020-00742-2
    Also: arXiv:1709.02592 [cs.DS]
  • Steven Chaplick, Magnús M. Halldórsson, Murilo Santos de Lima, Tigran Tonoyan: Query Minimization Under Stochastic Uncertainty. LATIN 2020: 181-193.
    https://doi.org/10.1007/978-3-030-61792-9_15
    Also: arXiv:2010.03517 [cs.DS], October 2020.
  • Thomas Erlebach, Michael Hoffmann, Murilo S. de Lima, Nicole Megow, Jens Schlöter:
    Untrusted Predictions Improve Trustable Query Policies
    arXiv:2011.07385 [cs.DS], November 2020.
  • Thomas Erlebach, Michael Hoffmann, Murilo S. de Lima:
    Round-Competitive Algorithms for Uncertainty Problems with Parallel Queries
    In: Proceedings of the 38th International Symposium on Theoretical Aspects of Computer Science (STACS 2021), Saarland Informatics Campus, Saarbrücken, Germany, March 16-19, 2021. LIPIcs 117, 27:1-27:18, 2021.
    Also: arXiv:2101.05032 [cs.DS]
  • Evripidis Bampis, Christoph Dürr, Thomas Erlebach, Murilo S. de Lima, Nicole Megow, Jens Schlöter
    Orienting (hyper)graphs under explorable stochastic uncertainty
    In: Proceedings of the 29th Annual European Symposium on Algorithms (ESA 2021), Lisbon, Portugal, September 6-8, 2021. LIPIcs, to appear.

Background

A survey of known results on computing with uncertainty (until 2015) can be found here:

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