Kaatee, Sander (2023) Analysis and Optimization of the Accelerated Argumentation-Based Learning Algorithm. Bachelor's Thesis, Artificial Intelligence.
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Abstract
Accelerated Argumentation-Based Learning (AABL) has been shown to have significant improvements in accuracy and learning speed when compared to other state-of-the-art machine learning techniques. Despite its promising performance, the algorithm has certain limitations and potential for optimization. In this paper, we provide an in-depth analysis of the AABL algorithm, examine its limitations, and propose possible optimizations. Three algorithms, the original implementation of AABL, Refactored Argumentation-Based Learning (RABL), and an implementation of AABL as it is described in the paper, are compared in various scenarios. The paper discusses the main differences between the algorithms and provides insights into the argumentation-based and machine learning aspects of AABL, as well as its limitations and potential for explainability.
| Item Type: | Thesis (Bachelor's Thesis) |
|---|---|
| Supervisor name: | Verheij, H.B. |
| Degree programme: | Artificial Intelligence |
| Thesis type: | Bachelor's Thesis |
| Language: | English |
| Date Deposited: | 16 Aug 2023 09:51 |
| Last Modified: | 16 Aug 2023 09:51 |
| URI: | https://fse.studenttheses.ub.rug.nl/id/eprint/31195 |
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