Two Compacted Models for Efficient Model-Based Diagnosis


  • Huisi Zhou Jilin University
  • Dantong Ouyang Jilin University
  • Xiangfu Zhao Yantai University
  • Liming Zhang Jilin University



Constraint Satisfaction And Optimization (CSO), Search And Optimization (SO)


Model-based diagnosis (MBD) with multiple observations is complicated and difficult to manage over. In this paper, we proposed two new diagnosis models, namely, the Compacted Model with Multiple Observations (CMMO) and the Dominated-based Compacted Model with Multiple Observations (D-CMMO), to solve the problem in which a considerable amount of time is needed when multiple observations are given and more than one fault is injected. Three ideas are presented in this paper. First, we propose to encode MBD with each observation as a subsystem and share as many system variables as possible to compress the size of encoded clauses. Second, we utilize the notion of gate dominance in the CMMO approach to compute Top-Level Diagnosis with Compacted Model (CM-TLD) to reduce the solution space. Finally, we explore the performance of our model using three fault models. Experimental results on the ISCAS-85 benchmarks show that CMMO and D-CMMO perform better than the state-of-the-art algorithms.




How to Cite

Zhou, H., Ouyang, D., Zhao, X., & Zhang, L. (2022). Two Compacted Models for Efficient Model-Based Diagnosis. Proceedings of the AAAI Conference on Artificial Intelligence, 36(4), 3885-3893.



AAAI Technical Track on Constraint Satisfaction and Optimization