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Computational argumentation is a promising research area, yet there is a gap between theoretical contributions and practical applications. Bridging this gap could potentially raise interest in this topic even more. We argue that one part of the bridge could be an open-source package of implementations of argumentation algorithms, visualised in a web interface. Therefore we present a new release of PyArg, providing various new argumentation-based functionalities – including multiple generators, a learning environment, implementations of theoretical papers and a showcase of a practical application – in a new interface with improved accessibility.

Reasoning under incomplete information is an important research direction in AI argumentation. Most computational advances in this direction have so-far focused on abstract argumentation frameworks. Development of computational approaches to reasoning under incomplete information in structured formalisms remains to-date to a large extent a challenge. We address this challenge by studying the so-called stability and relevance problems---with the aim of analyzing aspects of resilience of acceptance statuses in light of new information---in the central structured formalism of ASPIC+. Focusing on the case of the grounded semantics and an ASPIC+ fragment motivated through application scenarios, we develop exact ASP-based algorithms for stability and relevance in incomplete ASPIC+ theories, and pinpoint the complexity of reasoning about stability (coNP-complete) and relevance (Sigma_2^P-complete), further justifying our ASP-based approaches. Empirically, the algorithms exhibit promising scalability, outperforming even a recent inexact approach to stability, with our ASP-based iterative approach being the first algorithm proposed for reasoning about relevance in ASPIC+.

With the growing usage of artificial intelligence (AI) in daily life, explainable systems become more important. Explainable AI (XAI), which is a set of tools and frameworks to help you understand and interpret predictions made by AI, has risen in popularity due to this. Formal argumentation is a suitable tool that can be used to model contrastive explanations for (X)AI systems. In this thesis, the concept of contrastiveness will be modelled in various formal argumentation settings. The definition for contrastiveness is based on definitions found in literature from the field of the social sci- ences and humanities. First, an extensive literature research is conducted to find suitable definitions for contrastiveness, then those concepts are modelled in Dung’s abstract argumentation frameworks, preference-based argumentation frameworks and finally in the structured argumentation setting of ASPIC+. These definitions are then evaluated at the hand of examples based on real-life situations. This work successfully models various notions of contrastiveness in various formal argumentation set- tings and paves the way for the further study and application of contrastiveness in (argumentative) XAI.

In this thesis it is examined how argumentative dialogues can be adapted for use in Explainable Artificial Intelligence (XAI) and this thesis shows the possibilities on how XAI can be improved in terms of explanation transfer. This is achieved by creating an explanatory dialogue system which integrates findings from the social sciences. In this thesis, requirements of XAI for explanations are examined first. Subsequently, existing formal dialogue systems are discussed and finally, a new formal dialogue system is constructed. This new dialogue system implements various aspects: it allows for different user-types (lay users and experts), it allows for personalized explanations in terms of various question and explanation styles, plus, various explanation functions are usable. Lastly, a user study is performed in which the preferred amount of explanation of participants is measured. The results show that lay users tend to request more explanation than experts

Modeling contrastive explanations for the use in artificial intelligence (AI) applications is an important research branch within the field of explainable AI (XAI). However, most of the existing contrastive XAI approaches are not based on the findings in the literature from the social sciences on contrastiveness in human reasoning and human explanations. In this work we collect the various types of contrastiveness proposed in the literature and model these with formal argumentation. The result is a variety of argumentation-based methods for contrastive explanations, based on the available literature and applicable in a wide variety of AI-applications.

In this paper we study everyday explanations for classification tasks with formal argumentation. Everyday explanations describe how humans explain in day-to-day life, which is important when explaining decisions of AI systems to lay users. We introduce EVAX, a model-agnostic explanation method for classifiers with which contrastive, selected and social explanations can be generated. The resulting explanations can be adjusted in their size and retain high fidelity scores (an average of 0.95)

We explore the computational complexity of stability and relevance in incomplete argumentation frameworks (IAFs), abstract argumentation frameworks that encode qualitative uncertainty by distinguishing between certain and uncertain arguments and attacks. IAFs can be specified by, e.g., making uncertain arguments or attacks certain; the justification status of arguments in an IAF is determined on the basis of the certain arguments and attacks. An argument is stable if its justification status is the same in all specifications of the IAF. For arguments that are not stable in an IAF, the relevance problem is of interest: which uncertain arguments or attacks should be investigated for the argument to become stable? We redefine stability and define relevance for IAFs and study their complexity.

In this paper we discuss contrastive explanations for formal argumentation – the question why one argument (the fact) can be accepted, whilst another argument (the foil) cannot be accepted. We show under which conditions contrastive explanations in abstract argumentation are meaningful, and how argumentation allows us to make implicit foils explicit.

There has been an upswing in the research field of explainable artificial intelligence (XAI) of methods aimed at explaining opaque artificial intelligence (AI) systems and their decisions. A recent, promising approach involves the use of formal argumentation to explain machine learning (ML) applications. In this thesis we investigate that approach; we aim to gain understanding of the value of argumentation for XAI. In particular, we explore how well argumentation can produce everyday explanations. Everyday explanations describe how humans explain in day to-day life and are claimed to be important for explaining decisions of AI systems to end-users. First, we conceptually show how argumentative explanations can be posed as everyday explanations. Afterward, we demonstrate that current argumentative explanation methods compute explanations that already contain some, but not all properties of everyday explanations. Finally, we present everyday argumentative explanations, or EVAX, which is a model-agnostic method that computes local explanations for ML models. These explanations can be adjusted in their size and retain high fidelity scores (an average of 0.95) on four different datasets and four different ML models. In addition, the explanations incorporate the main characteristics of everyday explanations and help in achieving the objectives of XAI.