Simplifying A Logic Program Using Its Consequences

Authors: Jianmin Ji, Hai Wan, Ziwei Huo, Zhenfeng Yuan

IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Experimental Table 1 contains average sizes of consequences and GRS of P for different instances from 3 classes of NLPs and 2 classes of DLPs, and average times for computing these notions.
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 Ziwei Huo and Zhenfeng Yuan School of Software Sun Yat-sen University Guangzhou 510006, China wanhai@mail.sysu.edu.cn
Pseudocode Yes Algorithm 1: RSP,L(X)
Open Source Code No The paper provides a footnote with a URL (http://ss.sysu.edu.cn/%7ewh/simplifying.html). However, this URL leads to a personal project page and not directly to a source code repository for the methodology described in the paper.
Open Datasets Yes These benchmarks [15-Puzzle(N), Factoring(N), Schur Numbers(N), Mutex(D), RQBF(D)] were frequently used to compare the performance of ASP solvers [Denecker et al., 2009; Gebser et al., 2013].
Dataset Splits No The paper uses benchmark instances but does not provide specific details on how these instances were split into training, validation, or test sets.
Hardware Specification No The paper describes the implementation and experimental results but does not provide specific details about the hardware used for running the experiments.
Software Dependencies Yes We have implemented a program to compute GRS for programs grounded by gringo (version 4.4.0).
Experiment Setup No The paper describes the proposed algorithms and their theoretical properties but does not provide specific experimental setup details such as hyperparameters or specific configuration settings for their implementation beyond the software used.