Batch repair actions for automated troubleshooting

Hilla Shinitzky, Roni Stern

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


Repairing a set of components as a batch is often cheaper than repairing each of them separately. A primary reason for this is that initiating a repair action and testing the system after performing a repair action often incurs non-negligible overhead. However, most troubleshooting algorithms proposed to date do not consider the option of performing batch repair actions. In this work we close this gap, and address the combinatorial problem of choosing which batch repair action to perform so as to minimize the overall repair costs. We call this problem the Batch Repair Problem (BRP) and formalize it. Then, we propose several approaches for solving it. The first seeks to choose to repair the set of components that are most likely to be faulty. The second estimates the cost wasted by repairing a given set of components, and tried to find the set of components that minimizes these costs. The third approach models BRP as a Stochastic Shortest Path Problem (SSP-MDP) [1], and solves the resulting problem with a dedicated solver. Experimentally, we compare the pros and cons of the proposed BRP algorithms on a standard Boolean circuit benchmark and a novel benchmark from the Physiotherapy domain. Results show the clear benefit of performing batch repair actions with our BRP algorithms compared to repairing components one at a time.

Original languageEnglish
Article number103260
JournalArtificial Intelligence
StatePublished - 1 Jun 2020


  • Artificial Intelligence
  • Model-based diagnosis
  • Troubleshooting

ASJC Scopus subject areas

  • Language and Linguistics
  • Linguistics and Language
  • Artificial Intelligence


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