In formal argumentation theory, multiple argumentation-based explanation methods have been formulated based on ideas from social and cognitive science. However, these have not yet been empirically validated. One such idea is that information in an explanation needs to be related; in argumentation-based explanations, this has been captured as there being an attack path between arguments. This study describes and empirically validates two types of relatedness, related admissibility and directly related admissibility. This was done by instructing participants to select arguments from an argumentation framework to explain another argument in this framework. These explanations selected by the participants were compared to argumentation-based explanations that use relatedness. We found that both forms of relatedness are cognitively plausible. This gives insight into how argumentation theory can be applied in the real world to provide explanations.

This paper reports on an experiment on using case-based reasoning in Dutch administrative law. The use case is decision-support for human medical experts at the Dutch Central Office of Driving Certification who have to decide whether a citizen who applies for a driving licence is fit to drive. Case-based reasoning is investigated for this purposes because of its potential advantages over machine-learning approaches as regards transparency and explainability. Both traditional case-based reasoning, AI & Law models of precedential constraint and their combination are investigated on predictive accuracy relative to a large case base with more than 30.000 cases. A combined model is found to have the highest accuracy. The results indicate that human-in-the-loop support with a tool based on the combined model may be feasible, but whether this is indeed so requires further investigation.

We discuss explanations for formal (abstract and structured) argumentation – the question 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 selection based on necessity and sufficiency can be captured in our basic framework and discuss a real-life application.

We demonstrate a human-centred explanation method for rule-based automated decision-making systems in the legal domain. This explanation method uses a graph database to enable question-driven explanations and multimedia display. This way, we can tailor the explanation to the user. We show the implementation of our explanation method in a real-world scenario at the Dutch Tax and Customs Administration.

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+.

Logical argumentation is a well-known approach to modeling non-monotonic reasoning with conflicting information. In this paper we provide a comprehensive postulate-based study of properties of logical argumentation frameworks and a full characterization of their semantics and inference relations. In this way we identify well-behaved formal argumentative models of drawing logically justified inferences from a given set of possibly conflicting defeasible, as well as strict assumptions. Given some desiderata in terms of rationality postulates, we consider the conditions that an argumentation framework should fulfill for the desiderata to hold. One purpose of this approach is to assist designers to “plug-in” pre-defined formalisms according to actual needs. To this end, we present a classification of argumentation frameworks relative to the types of attacks they implement. In turn, for each class we determine which desiderata are satisfied. Our study is highly abstract, supposing only a minimal set of requirements on the considered underlying deductive systems, and in this way covering a broad range of formalisms, including classical, intuitionistic and modal logics.

Explainable artificial intelligence (XAI) has gained increasing interest in recent years in the argumentation community. In this paper we consider this topic in the context of logic-based argumentation, showing that the latter is a particularly promising paradigm for facilitating explainable AI. In particular, we provide two representations of abductive reasoning by sequent-based argumentation frameworks and show that such frameworks successfully cope with related challenges, such as the handling of synonyms, justifications, and logical equivalences.

We introduce PyArg, a Python-based solver and explainer for both abstract argumentation and ASPIC+. A large variety of extension-based semantics allows for flexible evaluation and several explanation functions are available.

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 provide a detailed analysis of the inference process induced by logical argumentation frameworks. The frameworks may be defined with respect to any propositional language and logic, different arguments that represent deductions in the logic, various support-based attack relations between arguments, and all the complete Dung-style semantics for the frameworks. We show that, ultimately, for characterizing the inference process with respect to a given framework, extension-based semantics may be divided into two types: single-extension and multiple-extension, which induce respective kinds of entailment relations. These entailments are further classified by the way they tolerate new information (nonmonotonicity-related properties) and maintain conflicts among arguments (inconsistency-related properties).

Enforcement, adjusting an argumentation framework such that a certain set of arguments becomes acceptable, is an important research topic within the study of dynamic argumentation, but one that has been little studied for structured argumentation. In this paper we study enforcement in a general structured argumentation setting. In particular, we study conditions on the argumentation setting and the knowledge base that ensure (or prevent) the acceptability of sets of formulas for structured argumentation frameworks.

We study the dynamic argumentation task of detecting stability: given a specific structured argumentation setting, can adding information change the acceptability status of some propositional formula? Detecting stability is not tractable for every input, but efficient computation is essential in practical applications. We present a sound approximation algorithm that recognises stability for many inputs in polynomial time and we discuss several of its properties. In particular, we show under which constraints on the input our algorithm is complete. The proposed algorithm is currently applied for fraud inquiry at the Dutch National Police - we provide an English demo version that also visualises the output of the algorithm.

We study properties related to relevance in non-monotonic consequence relations obtained by systems of structured argumentation. Relevance desiderata concern the robustness of a consequence relation under the addition of irrelevant information. For an account of what (ir)relevance amounts to we use syntactic and semantic considerations. Syntactic criteria have been proposed in the domain of relevance logic and were recently used in argumentation theory under the names of non-interference and crash-resistance. The basic idea is that the conclusions of a given argumentative theory should be robust under adding information that shares no propositional variables with the original database. Some semantic relevance criteria are known from non-monotonic logic. For instance, cautious monotony states that if we obtain certain conclusions from an argumentation theory, we may expect to still obtain the same conclusions if we add some of them to the given database. In this paper we investigate properties of structured argumentation systems that warrant relevance desiderata.

Reasoning with the maximally consistent subsets (MCS) of the premises is a well-known approach for handling contradictory information. In this paper we consider several variations of this kind of reasoning, for each one we introduce two complementary computational methods that are based on logical argumentation theory. The difference between the two approaches is in their ways of making consequences: one approach is of a declarative nature and is related to Dung-style semantics for abstract argumentation, while the other approach has a more proof-theoretical flavor, extending Gentzen-style sequent calculi. The outcome of this work is a new perspective on reasoning with MCS, which shows a strong link between the latter and argumentation systems, and which can be generalized to some related formalisms. As a by-product of this we obtain soundness and completeness results for the dynamic proof systems with respect to several of Dung’s semantics. In a broader context, we believe that this work helps to better understand and evaluate the role of logic-based instantiations of argumentation frameworks.