Confidence-based reasoning in stochastic constraint programming

Rossi R., Hnich B., TARIM Ş. A., Prestwich S.

Artificial Intelligence, vol.228, pp.129-152, 2015 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 228
  • Publication Date: 2015
  • Doi Number: 10.1016/j.artint.2015.07.004
  • Journal Name: Artificial Intelligence
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.129-152
  • Keywords: (α, θ)-solution, (α, θ)-solution set, Confidence interval analysis, Confidence-based reasoning, Global chance constraint, Sampled SCSP, Stochastic constraint programming
  • Hacettepe University Affiliated: Yes

Abstract

In this work we introduce a novel approach, based on sampling, for finding assignments that are likely to be solutions to stochastic constraint satisfaction problems and constraint optimisation problems. Our approach reduces the size of the original problem being analysed; by solving this reduced problem, with a given confidence probability, we obtain assignments that satisfy the chance constraints in the original model within prescribed error tolerance thresholds. To achieve this, we blend concepts from stochastic constraint programming and statistics. We discuss both exact and approximate variants of our method. The framework we introduce can be immediately employed in concert with existing approaches for solving stochastic constraint programs. A thorough computational study on a number of stochastic combinatorial optimisation problems demonstrates the effectiveness of our approach.