Preferred Reasoning in ABA by Cycle-Breaking
Authors: Kiet Nguyen Anh, Markus Ulbricht
IJCAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We test our implementation against the ASPfor ABA solver which convincingly won the ABA track of the ICCMA 23 competition. As it turns out, our algorithm outperforms ASPfor ABA on instances with small backdoor sizes. and 5.2 Empirical Evaluation and Table 1: Runtimes of our Algorithm 1 and ASPfor ABA in s |
| Researcher Affiliation | Academia | Kiet Ngyuen Anh1 , Markus Ulbricht2 Sca DS.AI Dresden/Leipzig, Leipzig University 1kietnguyen2023@hotmail.com 2mulbricht@informatik.uni-leipzig.de and This word was funded by the Federal Ministry of Education and Research of Germany and by S achsische Staatsministerium f ur Wissenschaft, Kultur und Tourismus in the programme Center of Excellence for AI-research Center for Scalable Data Analytics and Artificial Intelligence Dresden/Leipzig , project identification number: Sca DS.AI. |
| Pseudocode | Yes | Algorithm 1 Computing Admissible Candidates |
| Open Source Code | Yes | The source code for our algorithm can be found online1. 1https://github.com/kiet Github User/ ABA-Backdoor-Implementation |
| Open Datasets | Yes | As experimental data, 400 ABAFs from the ICCMA 2023 competition benchmark generator2 were used. 2https://iccma2023.github.io/benchmarks.html |
| Dataset Splits | No | As experimental data, 400 ABAFs from the ICCMA 2023 competition benchmark generator2 were used. |
| Hardware Specification | Yes | Computations were done using resources of the Leipzig University Computing Center. There we used the Paul Cluster with a memory limit of 32GB ram and with the CPU: 2x AMD EPYC 7713 @ 2.0GHz Turbo 3.7GHz (64 cores). |
| Software Dependencies | No | We used the ASP-solver Clingo [Gebser et al., 2018] to compute a minimal ACYCDG-backdoor. Since we have to compute the deductive closure Th D(S) several times for different assumption sets S, we utilized the Glucose SAT-solver [Audemard and Simon, 2018] based on Mini Sat [E en and S orensson, 2004]. This SAT-solver is accessed through the Py SAT library [Ignatiev et al., 2018]. |
| Experiment Setup | No | A timeout of 90s is used. |