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FastMapSVM for Predicting CSP Satisfiability

Authors Kexin Zheng, Ang Li, Han Zhang, T. K. Satish Kumar

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Author Details

Kexin Zheng
  • University of Southern California, Los Angeles, CA, USA
Ang Li
  • University of Southern California, Los Angeles, CA, USA
Han Zhang
  • University of Southern California, Los Angeles, CA, USA
T. K. Satish Kumar
  • University of Southern California, Los Angeles, CA, USA

Funding

This work at the University of Southern California is supported by DARPA under grant number HR001120C0157 and by NSF under grant number 2112533. The views, opinions, and/or findings expressed are those of the author(s) and should not be interpreted as representing the official views or policies of the sponsoring organizations, agencies, or the U.S. Government.

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Kexin Zheng, Ang Li, Han Zhang, and T. K. Satish Kumar. FastMapSVM for Predicting CSP Satisfiability. In 29th International Conference on Principles and Practice of Constraint Programming (CP 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 280, pp. 40:1-40:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023) https://doi.org/10.4230/LIPIcs.CP.2023.40

Abstract

Recognizing the satisfiability of Constraint Satisfaction Problems (CSPs) is NP-hard. Although several Machine Learning (ML) approaches have attempted this task by casting it as a binary classification problem, they have had only limited success for a variety of challenging reasons. First, the NP-hardness of the task does not make it amenable to straightforward approaches. Second, CSPs come in various forms and sizes while many ML algorithms impose the same form and size on their training and test instances. Third, the representation of a CSP instance is not unique since the variables and their domain values are unordered. In this paper, we propose FastMapSVM, a recently developed ML framework that leverages a distance function between pairs of objects. We define a novel distance function between two CSP instances using maxflow computations. This distance function is well defined for CSPs of different sizes. It is also invariant to the ordering on the variables and their domain values. Therefore, our framework has broader applicability compared to other approaches. We discuss various representational and combinatorial advantages of FastMapSVM. Through experiments, we also show that it outperforms other state-of-the-art ML approaches.

Subject Classification

ACM Subject Classification
  • Computing methodologies → Machine learning
Keywords
  • Constraint Satisfaction Problems
  • Machine Learning
  • FastMapSVM

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