Lifting Symmetry Breaking Constraints with Inductive Logic Programming
Authors: Alice Tarzariol, Martin Gebser, Konstantin Schekotihin
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We ran our tests on an Intel Xeon E5520 machine under Linux (Ubuntu 18.04.3), where each run was limited to 900 seconds time and 20 GB memory. In Table 1 to Table 4, the satisfiable instances are shown in grey rows, while the white rows contain unsatisfiable instances. The column BASE refers to CLINGO (v5.4.0) run on the original encoding, while ABK-DEF and ABK-SAT report results for the original encoding augmented with first-order constraints learned in the default or sat mode, respectively. |
| Researcher Affiliation | Academia | 1University of Klagenfurt, Austria 2Graz University of Technology, Austria alice.tarzariol@aau.at , martin.gebser@aau.at , konstantin.schekotihin@aau.at |
| Pseudocode | Yes | Algorithm 1: Approach to lift SBCs with ILP |
| Open Source Code | Yes | The implementation of our approach relies on CLINGO (consisting of the grounding and solving components GRINGO and CLASP), SBASS and ILASP, and is available at [Tarzariol et al., 2021]. ... [Tarzariol et al., 2021] A. Tarzariol, M. Gebser, and K. Schekotihin. ILP symmetry breaking. https://github. com/prosysscience/Symmetry Breaking with ILP, 2021. Accessed: 2021-05-21. |
| Open Datasets | No | The paper uses instances of combinatorial problems like the pigeon-hole problem and house-configuration problem [Friedrich et al., 2011], but it describes how instances are generated for training (S) and generalization (Gen) within their framework rather than providing concrete access information (link, DOI, specific repository, or formal citation with authors/year) for a pre-existing publicly available dataset. |
| Dataset Splits | No | The paper describes using instances from 'S' to generate examples for ILP and instances from 'Gen' to guarantee generalization, but it does not provide specific percentages, sample counts, or a detailed splitting methodology for training, validation, and test sets to reproduce data partitioning. |
| Hardware Specification | Yes | We ran our tests on an Intel Xeon E5520 machine under Linux (Ubuntu 18.04.3), where each run was limited to 900 seconds time and 20 GB memory. |
| Software Dependencies | Yes | The implementation of our approach relies on CLINGO (consisting of the grounding and solving components GRINGO and CLASP), SBASS and ILASP... The column BASE refers to CLINGO (v5.4.0) run on the original encoding... Since the mode declarations of ILASP (v4.0.0) do not support arithmetic built-ins such as <, we provide auxiliary predicates in ABK to simulate them. |
| Experiment Setup | Yes | The learned constraints depend on several aspects, e.g., the selected inputs or whether and how we apply the iterative learning approach... In every iteration, we exploit the constraints learned so far to tackle the remaining symmetries. To this end, we divide the hypothesis space for programs with three or more types of variables in the language bias: in the first ILP run, the mode declarations are restricted to two types of variables, say t1 and t2, and then they are progressively extended to further types from t3 to tk. |