Abstract
Compiling Model-Based Diagnosis to maximum satisfiability (MaxSAT) is currently a popular method because it can directly calculate the diagnosis. Although the method based on dominator component encoding can reduce the difficulty of the problem, with the increase of the system size, the complexity of the solution is also increasing. In this paper, we propose an efficient encoding method to solve this problem. The method makes several significant contributions. First, our strategy significantly reduces the size of the encoding required for constructing MaxSAT formulations in the offline phase, without the need for additional observations. Second, this strategy significantly decreases the number of clauses and variables through system observations, even when dealing with components that have uncertain output values. Last, our algorithm is applicable to both single and multiple observation diagnosis problems, without sacrificing the completeness of the solution set. Experimental results on ISCAS-85 benchmarks show that our algorithm outperforms the state-of-the-art algorithms on both single and multiple observation problems.
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Acknowledgements
This work was partially supported by the National Natural Science Foundation of China (Grant Nos. 62076108 and 62006094), the Scientific and Technological Developing Scheme of Jilin Province, China (Grant No. 20190701031GH), and the Key Project of the Education Department of Jilin Province, China (Grant No. JJKH20231175KJ).
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Jihong Ouyang is a professor of the College of Computer Science and Technology, Jilin University, China. She received her PhD degree in computer science and technology from Jilin University, China in 2005. Her current research interests include model-based diagnosis, SAT problem, and combinatorial optimization problem.
Sen Huang received MS degree from Zhejiang Normal University, China in 2022. He is now working toward the PhD degree in the College of Computer Science and Technology, Jilin University, China. His research interests focus on model-based diagnosis and SAT problem.
Jinjin Chi is a associate professor of the College of Computer Science and Technology, Jilin University, China. She received her PhD degree in computer science and technology from Jilin University, China in 2019. Her current research interests include model-based diagnosis and SAT problem.
Liming Zhang is a professor of the College of Computer Science and Technology, Jilin University, China. He received his PhD degree in computer science and technology from Jilin University, China in 2012. His current research interests include model-based diagnosis, SAT problem, and combinatorial optimization problem.
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Ouyang, J., Huang, S., Chi, J. et al. DVRE: dominator-based variables reduction of encoding for model-based diagnosis. Front. Comput. Sci. 19, 197349 (2025). https://doi.org/10.1007/s11704-024-40894-w
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DOI: https://doi.org/10.1007/s11704-024-40894-w