Two Compacted Models for Efficient Model-Based Diagnosis
Authors: Huisi Zhou, Dantong Ouyang, Xiangfu Zhao, Liming Zhang3885-3893
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on the ISCAS-85 benchmarks show that CMMO and D-CMMO perform better than the state-of-the-art algorithms. |
| Researcher Affiliation | Academia | Huisi Zhou,1,2 Dantong Ouyang,1,2 Xiangfu Zhao,3 Liming Zhang 1,2* 1 College of Computer Science and Technology, Jilin University, Changchun 130010, China 2 Key Laboratory of Symbolic Computation and Knowledge Engineering, Jilin University, Changchun 130010, China 3 School of Computer and Control Engineering, Yantai University, Yantai 264005, China |
| Pseudocode | Yes | Algorithm 1: Max SAT-based diagnostic algorithm with multiple observations |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code for the described methodology, nor does it include a link to a code repository. |
| Open Datasets | Yes | We use the systems from the ISCAS-85 benchmark Boolean circuit, which is used in the literature (Ignatiev et al. 2019; Marques-Silva et al. 2015; Siddiqi 2011; Feldman et al. 2010; De Kleer 2009). |
| Dataset Splits | No | The paper describes generating 'test cases' and 'benchmarks' ('300 benchmarks for each fault model') by mimicking faulty systems and flipping inputs, but it does not specify explicit train/validation/test dataset splits with percentages or sample counts, nor does it reference standard splits of an existing dataset for data partitioning. |
| Hardware Specification | Yes | Our experiments were conducted on Ubuntu 16.04 Linux with Intel Xeon E5-1607 @3.00G Hz, 16GB RAM. |
| Software Dependencies | No | The paper mentions 'RC2' (a Max SAT solver) and 'g++', but does not provide specific version numbers for these software components. |
| Experiment Setup | Yes | For the first observation, we randomly generate a set of instantiations of system inputs and randomly set K components faulty, with K ranging from 20 to 50... For the remainder of the observations, we generate the system inputs by flipping one input of the first observation... For each benchmark, we collect the execution runtime within 1000 s. |