A well-studied trait of human reasoning and decision-making is the ability to not only make decisions in the presence of contradictions, but also to explain why a decision was made, in particular if a decision deviates from what is expected by an inquirer who requests the explanation. In this paper, we examine this phenomenon, which has been extensively explored by behavioral economics research, from the perspective of symbolic artificial intelligence. In particular, we introduce four levels of intelligent reasoning in face of contradictions, which we motivate from a microeconomics and behavioral economics perspective. We relate these principles to symbolic reasoning approaches, using abstract argumentation as an exemplary method. This allows us to ground the four levels in a body of related previous and ongoing research, which we use as a point of departure for outlining future research directions.

Abstract argumentation has emerged as a method for non-monotonic reasoning that has gained popularity in the symbolic artificial intelligence community. In the literature, the different approaches to abstract argumentation that were refined over the years are typically evaluated from a formal logics perspective; an analysis that is based on models of economically rational decision-making does not exist. In this paper, we work towards addressing this issue by analysing abstract argumentation from the perspective of the rational man paradigm in microeconomic theory. To assess under which conditions abstract argumentation-based decision-making can be considered economically rational, we derive reference independence as a non-monotonic inference property from a formal model of economic rationality and create a new argumentation principle that ensures compliance with this property. We then compare the reference independence principle with other reasoning principles, in particular with cautious monotony and rational monotony. We show that the argumentation semantics as proposed in Dung’s seminal paper, as well as other semantics we evaluate, with the exception of naive semantics and the SCC-recursive CF2 semantics, violate the reference independence principle. Consequently, we investigate how structural properties of argumentation frameworks impact the reference independence principle and identify cyclic expansions (both even and odd cycles) as the root of the problem. Finally, we put reference independence into the context of preference-based argumentation and show that for this argumentation variant, which explicitly models preferences, reference independence cannot be ensured in a straight-forward manner.

In the symbolic artificial intelligence community, abstract argumentation with its semantics, i.e. approaches for defining sets of valid conclusions (extensions) that can be derived from argumentation graphs, is considered a promising method for non-monotonic reasoning. However, from a sequential perspective, abstract argumentation-based decision-making processes typically do not guarantee an alignment with common formal notions to assess consistency; in particular, abstract argumentation can, in itself, not enforce the satisfaction of relational principles such as reference independence (based on a key principle of microeconomic theory) and cautious monotony. In this paper, we address this issue by introducing different approaches to ensuring reference independence and cautious monotony in sequential argumentation: a reductionist, an expansionist, and an extension-selecting approach. The first two approaches are generically applicable, but may require comprehensive changes to the corresponding argumentation framework. In contrast, the latter approach guarantees that an extension of the corresponding argumentation framework can be selected to satisfy the relational principle by requiring that the used argumentation semantics is weakly reference independent or weakly cautiously monotonous, respectively, and also satisfies some additional straightforward principles. To highlight the relevance of the approach, we illustrate how the extension-selecting approach to reference independent argumentation can be applied to model (boundedly) rational economic decision-making.

In this paper, we introduce the notion of the degree of monotony to abstract argumentation, a well-established method for drawing inferences in face of conflicts in non-monotonic reasoning. Roughly speaking, the degree of monotony allows us, given an abstract argumentation semantics and an abstract argumentation framework to be as monotonic as possible, when iteratively drawing inferences and expanding the argumentation framework. However, we also show that when expanding an argumentation framework several times using so-called normal expansions, an agent may, at any given step, select a conclusion that has the highest degree of monotony w.r.t. the previous conclusion (considering the constraints of the semantics), but end up with a conclusion that has a suboptimal degree of monotony w.r.t. one or several conclusions that precede the previous conclusion. We formalize this observation as the degrees of monotony-dilemma.

In this paper, we introduce a formal framework for explaining change of inference in abstract argumentation, in particular in the context of iteratively drawing inferences from a sequence of normal expansions, with a focus on admissible set-based semantics. We then conduct a formal analysis, showing that given an initial argumentation framework and an extension that has been inferred from it, we can guarantee the existence of explanation arguments for the violation of monotony when inferring an extension from a normal expansion of the initial argumentation framework.

In this paper, we provide a formal framework for modeling the burden of persuasion in legal reasoning. The framework is based on abstract argumentation, a frequently studied method of non-monotonic reasoning, and can be applied to different argumentation semantics; it supports burdens of persuasion with arbitrary many levels, and allows for the placement of a burden of persuasion on any subset of an argumentation framework's arguments. Our framework can be considered an extension of related works that raise questions on how burdens of persuasion should be handled in some conflict scenarios that can be modeled with abstract argumentation. An open source software implementation of the introduced formal notions is available as an extension of an argumentation reasoning library.

In this article, we present a design methodology for argumentation-based health information systems. With a focus on the application of formal argumentation, the methodology aims at eliciting requirements in regard to argumentation reasoning behavior, knowledge and user models, and business logic on levels below and above the argumentation layer. We highlight specific considerations that need to be made dependent on the system type, i.e., for clinical decision-support systems, patient-facing systems, and administration systems. In addition, we outline challenges in regards to the design of argumentation-based intelligent systems for healthcare, considering the state of the art of argumentation research, health information systems, and software design methods. For each challenge, we outline a mitigation strategy.