Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Lazy-Grounding for Answer Set Programs with External Source Access
Authors: Thomas Eiter, Tobias Kaminski, Antonius Weinzierl
IJCAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | illustrative benchmarks show a clear advantage of the new algorithm for grounding-intense programs, which is a new perspective to make HEX more suitable for real-world application needs. ... we show experimental results which confirm the benefit of the new algorithm on illustrative benchmarks (Sec. 5). |
| Researcher Affiliation | Academia | Thomas Eiter, Tobias Kaminski, and Antonius Weinzierl Institut f ur Informationssysteme, Technische Universit at Wien Favoritenstraße 9-11, A-1040 Vienna, Austria EMAIL |
| Pseudocode | Yes | Algorithm 1: Lazy-Grounding HEX-Evaluation |
| Open Source Code | No | The paper mentions that the ALPHA lazy-grounding solver is 'freely available' and references benchmark implementations on GitHub, but it does not provide a direct link or explicit statement that the source code for the authors' specific implementation of the new algorithm described in the paper is available. |
| Open Datasets | Yes | We ran tests for randomly generated instances with n = 4, . . . , 24 persons and 2n items, where each individual preference (i, i ) uniformly occurs with 5% probability (Table 1). ... The benchmark instances and all results are available at http:/www.kr.tuwien.ac.at/research/projects/inthex/lazyhex. |
| Dataset Splits | No | The paper describes the generation of random instances for benchmarks but does not specify a train/validation/test split for a single dataset. It runs tests on sets of instances rather than splitting one large dataset. |
| Hardware Specification | Yes | The tests were performed on a Linux machine with two 12core AMD Opteron 6176 SE CPUs and 128 GB RAM. |
| Software Dependencies | No | The paper mentions several software components like 'ALPHA lazy-grounding solver', 'DLVHEX reasoner [Redl, 2016]', 'GRINGO and CLASP [Gebser et al., 2011b]', and 'HTCondor load distribution system3'. However, it does not provide specific version numbers for ALPHA, DLVHEX, or HTCondor, which are necessary for reproducible dependency description. |
| Experiment Setup | Yes | The timeout for each run was 300 secs and the memory limit 12 GB. ...Average runtimes of 10 instances per size (resp. 30 for benchmark #3) are reported in secs for computing all answer sets and one answer set (n=1); timeouts are in parentheses. ...We ran tests for randomly generated instances with n = 4, . . . , 24 persons and 2n items, where each individual preference (i, i ) uniformly occurs with 5% probability (Table 1). |