Learning to Break Symmetries for Efficient Optimization in Answer Set Programming
Authors: Alice Tarzariol, Martin Gebser, Konstantin Schekotihin, Mark Law
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We prove the correctness of our method and evaluate it on real-world optimization problems from the domain of automated configuration. Our experiments show significant improvements of optimization performance due to the learned first-order constraints. ... We evaluate the devised learning framework for optimization problems on optimization versions of the combinatorial problems addressed in (Tarzariol, Gebser, and Schekotihin 2022; Tarzariol et al. 2022) as well as on the fastfood problem (Denecker et al. 2009). Our experiments were performed on an Intel i7-3930K machine under Linux (Debian GNU/Linux 11), running ILASP (v4.3.1) as ILP and CLINGO (v5.5.2) as ASP system. The benchmarks and settings for reproducing our experiments can be found in https://github.com/prosysscience/Symmetry Breaking with ILP/tree/optimization. ... Table 1: Runtime results for CLINGO on: (i) baseline encodings; and (ii) baseline encodings augmented with learned constraints. #o and #s indicate the number of instances finished with a proven or unproven optimal solution, respectively. min, max, avg, and stdev provide the minimal, maximal, and average runtime in seconds along with the standard deviation over instances counted in #o (finished with a proven optimal solution). |
| Researcher Affiliation | Collaboration | Alice Tarzariol1, Martin Gebser1,2, Konstantin Schekotihin1, Mark Law3 1 University of Klagenfurt, Klagenfurt, Austria 2 Graz University of Technology, Graz, Austria 3 ILASP Limited, Grantham, UK {alice.tarzariol, martin.gebser, konstantin.schekotihin}@aau.at, mark@ilasp.com |
| Pseudocode | Yes | Algorithm 1: Method full-SBCs to compute examples for an instance i S of the optimization problem modeled by P |
| Open Source Code | Yes | The benchmarks and settings for reproducing our experiments can be found in https://github.com/prosysscience/Symmetry Breaking with ILP/tree/optimization. |
| Open Datasets | Yes | The benchmarks and settings for reproducing our experiments can be found in https://github.com/prosysscience/Symmetry Breaking with ILP/tree/optimization. ... As training sets S for the double, doublev, and triple instance families of PUP, we used two small yet representative instances per family. |
| Dataset Splits | No | The paper mentions training and testing instances but does not specify a separate validation split or explicit percentages/counts for data partitioning. |
| Hardware Specification | Yes | Our experiments were performed on an Intel i7-3930K machine under Linux (Debian GNU/Linux 11) |
| Software Dependencies | Yes | running ILASP (v4.3.1) as ILP and CLINGO (v5.5.2) as ASP system. |
| Experiment Setup | Yes | a time limit of 300 seconds for each run. ... a time limit of one hour per run. ... we repeat each learning run 120 times with different seeds |