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..
Plausibility Reasoning via Projected Answer Set Counting - A Hybrid Approach
Authors: Johannes K. Fichte, Markus Hecher, Mohamed A. Nadeem
IJCAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our approach combines existing systems with fast dynamic programming, which in our experiments shows advantages over existing ASP systems. |
| Researcher Affiliation | Academia | Johannes K. Fichte1 , Markus Hecher1 , Mohamed A. Nadeem2 1 TU Wien, Vienna, Austria 2 TU Dresden, Dresden, Germany |
| Pseudocode | Yes | The algorithm for hybrid solving is outlined in Listing 1... The algorithm PAt is given in Listing 2... |
| Open Source Code | Yes | We implemented algorithm Hyb PA, as given in Listings 1 and 2, into the system Hyb PA, which is publicly available at https://github.com/maliabd-al-majid/dpdb ASP. |
| Open Datasets | Yes | As instances, we take two sets (S1) ASP instances from the abstract argumentation competition and (S2) a prototypical ASP domain with reachability and use of transitive closure on real-world graphs. ... S1 contains instances of the ICCMA 17 competition [Lagniez et al., 2021]. ... For S2, we used real-world graphs of public transport networks from all over the world, which were used in the PACE 16 and 17 challenges [Dell et al., 2017]. |
| Dataset Splits | No | The paper does not provide specific training/validation/test dataset splits, percentages, or absolute counts for data partitioning. |
| Hardware Specification | Yes | All our solvers ran on a cluster consisting of 12 nodes. Each node of the cluster is equipped with two Intel Xeon E5-2650 CPUs, where each of these 12 physical cores runs at 2.2 GHz clock speed and has access to 256 GB shared RAM. |
| Software Dependencies | Yes | Our system builds upon clingo 5.5.1 [Gebser et al., 2009] and the tool dpdb [Fichte et al., 2021b] for handling table manipulations during dynamic programming via database management system Postgre SQL version 12.9. Hyb PA is written in Python3 and uses the decomposition tool htd [Abseher et al., 2017]... Results are gathered on Ubuntu 16.04.1 LTS powered on kernel 4.4.0-139 with hyperthreading disabled using version 3.7.6 of Python3. |
| Experiment Setup | Yes | The constant thresholdhybrid is set to 1000 and thresholdabstract is set to 8, allowing for aggressive abstractions. Then, for solving projected counting, Line 6 invokes clingo with options -q and --project... Run times larger than 1,800 seconds, respectively, count as timeout and main memory (RAM) was restricted to 60GB. |