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 [1].
Bipolar Abstract Dialectical Frameworks Are Covered by Kleene’s Three-valued Logic
Authors: Ringo Baumann, Maximilian Heinrich
IJCAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | For this very first experiment, we considered bipolar ADFs with a number of statements between 1 and 12. For each number, we generated at least 100 test instances, i.e. 100 ADFs with different bipolar acceptance formulae. The tests were run on an Ubuntu desktop with an Intel i5-6400 CPU and 32 Gi B RAM. The implemented Python script systematically generates and checks all three-valued interpretations v according to the specific semantical requirements, e.g. v i ΓD(v) in the case of admissible semantics. For a particular statement s, the script calculates ΓD(v)[s] in two different ways: via the classical consensus, i.e. with the help of two-valued completions, and via applying Kleene s threevalued logic. We measured the time required for each type of calculation, considering a 30 minute limit. The results for admissible, complete and preferred semantics are depicted in Table 3. |
| Researcher Affiliation | Academia | Ringo Baumann1,2 , Maximilian Heinrich3 1Computer Science Institute, Leipzig University, Germany 2Center for Scalable Data Analytics and Artificial Intelligence (Sca DS.AI) Dresden/Leipzig, Germany 3Intelligent Information Systems, Bauhaus-Universit at Weimar, Germany EMAIL, EMAIL |
| Pseudocode | No | The paper does not contain pseudocode or a clearly labeled algorithm block. |
| Open Source Code | Yes | To ensure reproducability, we provide access to our repository4, containing the solver, test cases, and other relevant technical details. 4https://github.com/kmax-tech/IJCAI-23 |
| Open Datasets | No | For this very first experiment, we considered bipolar ADFs with a number of statements between 1 and 12. For each number, we generated at least 100 test instances, i.e. 100 ADFs with different bipolar acceptance formulae. |
| Dataset Splits | No | The paper describes generating test instances but does not specify dataset splits for training, validation, or testing in the typical sense of supervised learning. |
| Hardware Specification | Yes | The tests were run on an Ubuntu desktop with an Intel i5-6400 CPU and 32 Gi B RAM. |
| Software Dependencies | No | The paper mentions an "implemented Python script" but does not provide specific version numbers for Python or any libraries used. |
| Experiment Setup | Yes | For this very first experiment, we considered bipolar ADFs with a number of statements between 1 and 12. For each number, we generated at least 100 test instances, i.e. 100 ADFs with different bipolar acceptance formulae. The implemented Python script systematically generates and checks all three-valued interpretations v according to the specific semantical requirements, e.g. v i ΓD(v) in the case of admissible semantics. For a particular statement s, the script calculates ΓD(v)[s] in two different ways: via the classical consensus, i.e. with the help of two-valued completions, and via applying Kleene s three-valued logic. We measured the time required for each type of calculation, considering a 30 minute limit. |