80 research outputs found
Contrastive Explanations for Argumentation-Based Conclusions
In this paper we discuss contrastive explanations for formal argumentation -
the question why a certain argument (the fact) can be accepted, whilst another
argument (the foil) cannot be accepted under various extension-based semantics.
The recent work on explanations for argumentation-based conclusions has mostly
focused on providing minimal explanations for the (non-)acceptance of
arguments. What is still lacking, however, is a proper argumentation-based
interpretation of contrastive explanations. We show under which conditions
contrastive explanations in abstract and structured argumentation are
meaningful, and how argumentation allows us to make implicit foils explicit
Epistemic Effects of Scientific Interaction: Approaching the Question with an Argumentative Agent-Based Model
The question whether increased interaction among scientists is beneficial or harmful for their efficiency in acquiring knowledge has in recent years been tackled by means of agent-based models (ABMs) (e.g. Zollman 2007, 2010; Grim 2009; Grim et al. 2013). Nevertheless, the relevance of some of these results for actual scientific practice has been questioned in view of specific parameter choices used in the simulations (Rosenstock et al. 2016). In this paper we present a novel ABM that aims at tackling the same question, while representing scientific interaction in terms of argumentative exchange. In this way we examine the robustness of previously obtained results under different modeling choices
Modeling Contrastiveness in Argumentation.
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
Human-centred explanation of rule-based decision-making systems in the legal domain
We propose a human-centred explanation method for rule-based automated
decision-making systems in the legal domain. Firstly, we establish a conceptual
framework for developing explanation methods, representing its key internal
components (content, communication and adaptation) and external dependencies
(decision-making system, human recipient and domain). Secondly, we propose an
explanation method that uses a graph database to enable question-driven
explanations and multimedia display. This way, we can tailor the explanation to
the user. Finally, we show how our conceptual framework is applicable to a
real-world scenario at the Dutch Tax and Customs Administration and implement
our explanation method for this scenario.Comment: This is the full version of a demo at the 36th International
Conference on Legal Knowledge and Information Systems (JURIX'23
Modeling Contrastiveness in Argumentation.
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
A Basic Framework for Explanations in Argumentation
We discuss explanations for formal (abstract and structured) argumentation-the question of whether and why a certain argument or claim can be accepted (or not) under various extension-based semantics. We introduce a flexible framework, which can act as the basis for many different types of explanations. For example, we can have simple or comprehensive explanations in terms of arguments for or against a claim, arguments that (indirectly) defend a claim, the evidence (knowledge base) that supports or is incompatible with a claim, and so on. We show how different types of explanations can be captured in our basic framework, discuss a real-life application and formally compare our framework to existing work
Accessible Algorithms for Applied Argumentation
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
- …