Solving Advanced Argumentation Problems with Answer-Set Programming
Authors: Gerhard Brewka, Martin Diller, Georg Heissenberger, Thomas Linsbichler, Stefan Woltran
AAAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We carried out experiments to compare the performance of our approach with existing systems for ADFs. Our experiments show the potential and improved performance of our encodings. |
| Researcher Affiliation | Academia | Gerhard Brewka Universit at Leipzig, Leipzig, Germany Martin Diller, Georg Heissenberger, Thomas Linsbichler, and Stefan Woltran TU Wien, Vienna, Austria |
| Pseudocode | No | The paper presents logical rules and program fragments but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The system for ADF reasoning is available at https://www.dbai.tuwien.ac.at/proj/adf/yadf/. The encodings for GRAPPA are available as part of a larger system: https://www.dbai.tuwien.ac.at/ proj/adf/grappavis/. |
| Open Datasets | No | The paper describes using generated ADFs and public transport networks as input, but does not provide concrete access information (link, DOI, repository, or formal citation) for a publicly available dataset. |
| Dataset Splits | No | The paper does not explicitly provide training/test/validation dataset splits (percentages, counts, or predefined splits) for its experiments. |
| Hardware Specification | Yes | Experiments were carried out on a 48 GB Debian (8.5) machine with 8 Intel Xeon processors (2.33 GHz). |
| Software Dependencies | Yes | To make use of the encodings for reasoning, these need to be fed to an ASP solver such as clingo (Gebser et al. 2011)... For QADF we used version 0.3.2 with bloqqer 035 (Biere, Lonsing, and Seidl 2011) and Dep QBF 4.0 (Lonsing and Biere 2010). YADF is version 0.1.0 with the rule decomposition tool lpopt (Bichler, Morak, and Woltran 2016) and clingo 4.4.0. |
| Experiment Setup | Yes | To generate ADFs, we first used a grid-based ADF generator which has been employed in previous evaluations (Diller, Wallner, and Woltran 2014). Here statements have as parents a subset of 8 possible neighbors of a randomly generated grid of width 7. Acceptance conditions are generated by connecting parents via or . Probabilities determine the choice of these connectives and whether parents appear negated or are replaced by truth constants. We also wrote our own graph-based generator which takes a directed graph as input and generates an ADF inheriting the structure of the graph... Experiments were carried out on a... (with time-out of 600 seconds) |