On Elementary Loops and Proper Loops for Disjunctive Logic Programs
Authors: Jianmin Ji, Hai Wan, Peng Xiao
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | At last, some experiments show that both elementary loops and proper loops could be replaced by their weak versions in practice. In this section, to compare various notions of loops in practice, i.e., elementary loops (EL), EL (P) (EL ), weak elementary loops (WEL), proper loops (PL), PL (P) (PL ), and weak proper loops (WPL), we count numbers of loops in these classes on DLPs in the benchmark. The experiments were run on a Linux machine with AMD A10-5800K (3.8GHz) CPU and 3.3GB RAM, limiting each run to 1 hour. Table 12 shows average numbers of loops for different notions on 63 DLPs from 7 classes3, which were frequently used to compare the performance of ASP solvers for DLPs (Denecker et al. 2009; Gebser, Kaufmann, and Schaub 2012; 2013). |
| Researcher Affiliation | Academia | Jianmin Ji School of Computer Science and Technology University of Science and Technology of China Hefei 230027, China jianmin@ustc.edu.cn Hai Wan and Peng Xiao School of Software Sun Yat-sen University Guangzhou 510006, China wanhai@mail.sysu.edu.cn |
| Pseudocode | Yes | Algorithm 1: EL (L, P) Algorithm 2: EWEL(P, E) Algorithm 3: HWEF (P) Algorithm 4: PL (L, P) |
| Open Source Code | No | The paper provides a link (http://ss.sysu.edu.cn/%7ewh/properloopdlp.html) which seems to be for supplementary materials and data, but does not explicitly state that it hosts the source code for the methodology described in the paper. |
| Open Datasets | Yes | In this section, to compare various notions of loops in practice, i.e., elementary loops (EL), EL (P) (EL ), weak elementary loops (WEL), proper loops (PL), PL (P) (PL ), and weak proper loops (WPL), we count numbers of loops in these classes on DLPs in the benchmark. The experiments were run on a Linux machine with AMD A10-5800K (3.8GHz) CPU and 3.3GB RAM, limiting each run to 1 hour. Table 12 shows average numbers of loops for different notions on 63 DLPs from 7 classes3, which were frequently used to compare the performance of ASP solvers for DLPs (Denecker et al. 2009; Gebser, Kaufmann, and Schaub 2012; 2013). DLPs in the classes of Sokoban and SCore-disjunctiveloop are HCF programs, while others are not HCF. |
| Dataset Splits | No | The paper does not describe any train/validation/test dataset splits as its experiments do not involve training machine learning models. |
| Hardware Specification | Yes | The experiments were run on a Linux machine with AMD A10-5800K (3.8GHz) CPU and 3.3GB RAM |
| Software Dependencies | No | The paper mentions general tools like 'ASP solvers for DLPs' and 'clasp D' but does not provide specific version numbers for any software dependencies used in their experiments. |
| Experiment Setup | Yes | The experiments were run on a Linux machine with AMD A10-5800K (3.8GHz) CPU and 3.3GB RAM, limiting each run to 1 hour. |