Active Learning of Combinatorial Features for Interactive Optimization

  • Paolo Campigotto
  • Andrea Passerini
  • Roberto Battiti
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6683)


We address the problem of automated discovery of preferred solutions by an interactive optimization procedure. The algorithm iteratively learns a utility function modeling the quality of candidate solutions and uses it to generate novel candidates for the following refinement. We focus on combinatorial utility functions made of weighted conjunctions of Boolean variables. The learning stage exploits the sparsity-inducing property of 1-norm regularization to learn a combinatorial function from the power set of all possible conjunctions up to a certain degree. The optimization stage uses a stochastic local search method to solve a weighted MAX-SAT problem. We show how the proposed approach generalizes to a large class of optimization problems dealing with satisfiability modulo theories. Experimental results demonstrate the effectiveness of the approach in focusing towards the optimal solution and its ability to recover from suboptimal initial choices.


Utility Function Multiobjective Optimization Gold Solution Interactive Optimization Stochastic Local Search 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Paolo Campigotto
    • 1
  • Andrea Passerini
    • 1
  • Roberto Battiti
    • 1
  1. 1.DISI - Dipartimento di Ingegneria e Scienza dell’InformazioneUniversità degli Studi di TrentoItaly

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