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Explainable AI: On the Reasoning of Symbolic and Connectionist Machine Learning Techniques

Steging, Cor (2018) Explainable AI: On the Reasoning of Symbolic and Connectionist Machine Learning Techniques. Master's Thesis / Essay, Artificial Intelligence.


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Modern connectionist machine learning approaches outperform classical rule-based systems in problems such as classification tasks. The major downside of the connectionist approach, however, is the lack of an explanation for the decisions that it makes, which is a quality that the rule-based systems are known for. The new sub-field of Explainable Artificial Intelligence (XAI) therefore aims to combine the performance of the connectionist approach with the understandability of knowledge systems. Examples of such hybrid systems extract rules from neural networks, which can explain how a network comes to a particular decision. However, this is built upon the premise that the reasoning of a neural network that performs well is sound. Previous research has suggested that this is not necessarily true; a high performance does not guarantee a sound rationale. This study, therefore, aims to investigate how well machine learning systems are able to correctly internalize the underlying structure or rules of a dataset. This is accomplished with the use of artificial datasets, whose rules and structure are known beforehand. The results confirm that a high classification accuracy can be obtained without having to have learned all of the rules that define the dataset. Furthermore, the systems are able to internalize certain rules better than others. When multiple rules define the dataset, interaction effects occur that cause the systems to be less proficient in internalizing the rules.

Item Type: Thesis (Master's Thesis / Essay)
Supervisor name: Verheij, B. and Schomaker, L.R.B.
Degree programme: Artificial Intelligence
Thesis type: Master's Thesis / Essay
Language: English
Date Deposited: 12 Jul 2018
Last Modified: 20 Jul 2018 09:52

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