Model-Based Diagnosis with Uncertain Observations
Authors: Dean Cazes, Meir Kalech2766-2773
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental evaluation shows that this third algorithm can be very effective in cases where the number of faults is small and the uncertainty over the observations is not large. |
| Researcher Affiliation | Academia | Dean Cazes, Meir Kalech Ben-Gurion University of the Negev, Israel deanc@post.bgu.ac.il, kalech@bgu.ac.il |
| Pseudocode | Yes | Algorithm 1 lists a pseudo code for O2D. Algorithm 2 lists a pseudo code for D2O. Algorithm 3 is an extension of O2D, called most likely diagnosis (Mst Like Diag), that returns the most probable diagnoses without finding all diagnoses. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | To evaluate our algorithms for the abductive diagnosis form, we used a system from the 74XXX benchmark Boolean circuit an ALU called 74182, having |O| = R = 5 outputs, SYSIN = 9 inputs and COMPS = 18 components. All observations were selected from Feldman et al. s (2010) known benchmark, a total of 250 observations, equally divided to five minimal cardinality values of 1-5. ...we used Barinel (Abreu, Zoeteweij, and van Gemund 2009) algorithm... Next, we evaluated our algorithms using real bugs from Apache Commons-Lang, an open-source software project. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, or detailed splitting methodology) needed to reproduce the data partitioning. |
| Hardware Specification | No | The paper does not provide specific hardware details (GPU/CPU models, processor types, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions using 'Barinel' algorithm but does not specify its version number or any other software dependencies with versions. |
| Experiment Setup | Yes | For each algorithm, O2D (Alg. 1), D2O (Alg. 2) and Mst Like Diag (Alg. 3), observation and value of P, we collected the average execution runtime. ... Then, we set a probability P for an observed output to be wrong. ... we created a combination of n components and m tests, where 7 n, m 13. For each combination we created 20 random matrices and error vectors having the combination properties. For each matrix file with m tests, we executed all three algorithms with a fixed number of u tests with uncertain observation over the result of the test, where 7 u m. In addition, the third algorithm was executed with different faulty output probabilities ([0.1, 0.5]). |