Counter-Transitivity in Argument Ranking Semantics
Authors: Fuan Pu, Jian Luo, Guiming Luo
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this work, we develop a formal theory about the argument ranking semantics based on this principle. Three approaches, i.e., quantity-based, quality-based and the unity of them, are defined to implement the principle. Then, we show an iterative refinement algorithm for capturing the ranking on arguments based on the recursive nature of the principle. |
| Researcher Affiliation | Academia | Fuan Pu, Jian Luo and Guiming Luo Tsinghua National Laboratory for Information Science and Technology School of Software, Tsinghua University, Beijing 100084, China Pu.Fuan@gmail.com, j-luo10@mails.tsinghua.edu.cn, gluo@tsinghua.edu.cn |
| Pseudocode | Yes | Algorithm 1: Solving ranking semantics by iteration |
| Open Source Code | No | The paper does not provide any statement or link regarding the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and uses a small, manually defined example for illustration, not a publicly available dataset for empirical evaluation. The example given is 'Example 1. Consider the AF with X = {x, y1, y2, y3, z} and R = {x Ryi, yi Rz}, i = 1, 2, 3.' |
| Dataset Splits | No | The paper is theoretical and uses a small, illustrative example rather than conducting experiments with datasets that would require training, validation, or test splits. |
| Hardware Specification | No | The paper focuses on theoretical development and does not describe any hardware specifications, as it does not report on computational experiments that would require them. |
| Software Dependencies | No | The paper does not provide any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and describes an algorithm with an illustrative example, but it does not provide details about an experimental setup, hyperparameters, or system-level training settings. |