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As we engage in a debate with other parties, it is usual that several issues might come under discussion. Many of these issues are interrelated by the topic they address, while others represent a departure from the focus of the discussion. In this work, we propose to extend Dung’s abstract argumentation frameworks by decorating arguments with a set...
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... attack relation between these arguments is depicted in Figure 7, with the general hashcloud of Figure 8. To keep our analysis simple the attacks graph only shows the relevant hashtags. ...
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Ranking-based semantics for Abstract Argumentation Frameworks represent a well-established concept used for sort-ing a group of arguments from the most to the least acceptable. This paper describes a ranking-based semantics that makes useof power indexes such as the Shapley Value and the Banzhaf Power Index. This power index-based semantics is para...
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... However, these approaches often neglect the dynamic nature of belief updating during dialogues. On a similar thread, our work fits well within the literature on argumentation-based explainable AI (Fan and Toni 2015;Shams et al. 2016;Fan 2018;Collins, Magazzeni, and Parsons 2019;Budán et al. 2020;Dennis and Oren 2022;Rago, Li, and Toni 2023;Vasileiou et al. 2024). While these approaches provide a solid foundation for argumentationbased explanations, they do not explicitly focus on approximating the human users model, which is central to this paper. ...
Explainable AI is increasingly employing argumentation methods to facilitate interactive explanations between AI agents and human users. While existing approaches typically rely on predetermined human user models, there remains a critical gap in dynamically learning and updating these models during interactions. In this paper, we present a framework that enables AI agents to adapt their understanding of human users through argumentation-based dialogues. Our approach, called Persona, draws on prospect theory and integrates a probability weighting function with a Bayesian belief update mechanism that refines a probability distribution over possible human models based on exchanged arguments. Through empirical evaluations with human users in an applied argumentation setting, we demonstrate that Persona effectively captures evolving human beliefs, facilitates personalized interactions, and outperforms state-of-the-art methods.
... On a similar thread, our work fits well within the literature on argumentation-based explainable AI (Čyras et al. 2021). However, a big difference with most existing approaches within that space (Fan and Toni 2015;Collins, Magazzeni, and Parsons 2019;Oren, van Deemter, and Vasconcelos 2020;Budán et al. 2020;Rago, Li, and Toni 2023) is that they are based on forms of abstract argumentation, which in our specific setting offers limited expressivity as the internal structure of arguments is ignored. In a practical explanatory dialogue setting with implementations for user studies (such as in our case), one must know and express the contents of the arguments conveyed, and how they can be used to generate new arguments and counterarguments. 1 In similar spirit, Dennis and Oren (2022) proposed a framework for explaining the behavior of BDI systems. ...
We present a novel framework designed to extend model reconciliation approaches, commonly used in human-aware planning, for enhanced human-AI interaction. By adopting a structured argumentation-based dialogue paradigm, our framework enables dialectical reconciliation to address knowledge discrepancies between an explainer (AI agent) and an explainee (human user), where the goal is for the explainee to understand the explainer's decision. We formally describe the operational semantics of our proposed framework, providing theoretical guarantees. We then evaluate the framework's efficacy ``in the wild'' via computational and human-subject experiments. Our findings suggest that our framework offers a promising direction for fostering effective human-AI interactions in domains where explainability is important.
... This work demonstrates the potential of argumentation-based approaches for facilitating collaborative decision-making and consensus-building in multi-agent settings. Argumentation-based explanation have also gained a lot of traction (Fan and Toni 2015;Shams et al. 2016;Fan 2018;Collins, Magazzeni, and Parsons 2019;?;Budán et al. 2020;Dennis and Oren 2022;Rago, Li, and Toni 2023). These works primarily focus on explanations whose justification is provided through argumentation semantics using specific dialogue formalizations, establishing an equivalence between the dialogues and the argumentation semantics. ...
Explainable AI Planning (XAIP) aims to develop AI agents that can effectively explain their decisions and actions to human users, fostering trust and facilitating human-AI collaboration. A key challenge in XAIP is model reconciliation, which seeks to align the mental models of AI agents and humans. While existing approaches often assume a known and deterministic human model, this simplification may not capture the complexities and uncertainties of real-world interactions. In this paper, we propose a novel framework that enables AI agents to learn and update a probabilistic human model through argumentation-based dialogues. Our approach incorporates trust-based and certainty-based update mechanisms, allowing the agent to refine its understanding of the human's mental state based on the human's expressed trust in the agent's arguments and certainty in their own arguments. We employ a probability weighting function inspired by prospect theory to capture the relationship between trust and perceived probability, and use a Bayesian approach to update the agent's probability distribution over possible human models. We conduct a human-subject study to empirically evaluate the effectiveness of our approach in an argumentation scenario, demonstrating its ability to capture the dynamics of human belief formation and adaptation.
... Despite the expressive power and generality of Dung's framework, in some cases it is difficult to accurately model domain knowledge by an AF in a natural and easy-to-understand way. For this reason, Dung's framework has been extended by introducing further constructs, such as preferences [5,51], weights [18,19,17], supports [28,25,44], topics [21], and constraints [29,9,55,2], to achieve more comprehensive, natural, and compact ways for representing useful relationships among arguments. ...
Dung’s Argumentation Framework (AF) has been extended in several directions. An interesting extension, among others, is the Epistemic AF (EAF) which allows representing the agent’s belief by means of epistemic constraints. In particular, an epistemic constraint is a propositional formula over labeled arguments (e.g. in(a), out(c)) extended with the modal operators K and M that intuitively state that the agent believes that a given formula is certainly or possibly true, respectively. In this paper, focusing on EAF, we investigate the complexity of the possible and necessary variants of three canonical problems in abstract argumentation: verification, existence, and non-empty existence. Moreover, we explore the relationship between EAF and incomplete AF (iAF), an extension of AF where arguments and attacks may be uncertain. Our complexity analysis shows that the verification problem in iAF can be naturally reduced to the verification in EAF, while it turns out that a similar result cannot hold for the necessary (non-empty) existence problem.
... Following that paper, various extensions have been proposed, enabling attacks to be directed towards attacks [5], adding support [17], preference [3] or other types of relations [34], adding weights to arguments [2] or attacks [24], associating arguments with values [8], or imposing hierarchies on arguments [35]. However, little attention has been paid to what we call the scope of an argument, i.e., the class of entities that an argument can be applied to, despite the fact that it has been studied in other related areas, such as discourse analysis [38], linguistic argumentation [42] and political argumentation [41]. ...
... Finally, Budán et al. in [10], the authors offered the formalization of an abstract argumentation framework that considers a set of interrelated topics used to decorate arguments. One of the contributions is the examination of new argumentation semantics that consider these topics to obtain the accepted arguments. ...
We present a novel argumentation-based method for finding and analyzing communities in social media on the Web, where a community is regarded as a set of supported opinions that might be in conflict. Based on their stance, we identify argumentative coalitions to define them; then, we apply a similarity-based evaluation method over the set of arguments in the coalition to determine the level of cohesion inherent to each community, classifying them appropriately. Introducing conflict points and attacks between coalitions based on argumentative (dis)similarities to model the interaction between communities leads to considering a meta-argumentation framework where the set of coalitions plays the role of the set of arguments and where the attack relation between the coalitions is assigned a particular strength which is inherited from the arguments belonging to the coalition. Various semantics are introduced to consider attacks’ strength to particularize the effect of the new perspective. Finally, we analyze a case study where all the elements of the formal construction of the formalism are exercised.
... In Budán et al. [5], a framework and its corresponding semantics in which arguments are associated with abstract labels denoting that a piece of reasoning may refer to different topics in the context of argumentation was introduced. For instance, an argument may refer to a virus pandemia, to the quarantine, and to the welfare state, while another argument may refer to virus pandemia and music online streaming. ...
... This idea leads to restricted forms of acceptability of arguments, where only defenders in the neighborhood are allowed. While in [5] a single, universal degree of proximity is applied to the whole framework yielding an admissible extension, here this attribute is defined for every individual argument. In Figure 1 argument C is required to be in the neighborhood of A in order to provide a defense for A. Argument D may have a different neighborhood, and if F is not in it, then D has no close defense or, in other words, the defense provided by F is not relevant for D. The previous discussion leads naturally to a novel admissible semantics that we will address in this work. ...
... We will present new forms of proximity-based semantics using hashtagged argumentation frameworks [5] as a starting point because it provides the elements for the consideration of a notion of distance. A hashtagged framework extends the representation capability of abstract frameworks by adding the topics addressed or referred to by the arguments. ...
The introduction of abstract argumentation has allowed the study of many exciting characteristics of the argumentation process. Nevertheless, while helpful in many aspects, abstraction diminishes the knowledge representation capabilities available to describe naturally occurring features of argumentative dialogues. One of these elements is the consideration of the topics involved in a discussion. In studying dialogical processes, participants recognize that some topics are closely related to the original issue, while others are more distant to the central subject or simply refer to unrelated matters. Consequently, it is reasonable to study different argumentation semantics that consider the focus of a discussion to evaluate acceptability. In this work, we will introduce the necessary representational elements required to reflect the focus of a discussion, and we will propose an extension of the semantics for multi-topic abstract argumentation frameworks acknowledging that every argument has its own zone of relevance in the argumentation framework, leading to a concept of a neighborhood of legitimate defenses. Furthermore, other semantic elaborations are defined and discussed around this structure.
... The resulting Bipolar Argumentation Frameworks (BAFs) allow to model situations in which an argument can, for instance, reinforce another argument providing more reasons to believe in it; moreover, they adapt Dung's acceptability semantics adding the consideration of the support relationship. Also, there exist some argumentative formalisms that represent the attributes associated to arguments providing more information to determine arguments acceptability [15,19,20,23], while others consider relations between the arguments to calculate the acceptability and safety of a given set of arguments [10]. ...
The notion of similarity has been studied in many areas of Computer Science; in a general sense, this concept is defined to provide a measure of the semantic equivalence between two pieces of knowledge, expressing how “close” their meaning can be regarded. In this work, we study similarity as a tool useful to improve the representation of arguments, the interpretation of the relations between arguments, and the semantic evaluation associated with the arguments in the argumentative process. In this direction, we present a novel mechanism to determine the similarity between two arguments based on descriptors representing particular aspects associated with these arguments. This mechanism involves a comparison process influenced by the context in which the process develops, where this context provides the relevant aspects that need to be analyzed in the application domain. Then, we use this similarity measure as a quantity to compute the result of attacks and supports in the argumentation process. These valuations, applied to a Bipolar Argumentation Frameworks, allowed us to refine the argument relations, providing the tools to establish a family of new argumentation semantics that considers the similarity between arguments as a crucial part for the argumentation process.
The acceptability of attacks plays a key role in defining semantics of higher-order argumentation frameworks (HO-AF). Several approaches have been put forward to describe the recursive acceptability of attacks. The diversity, on the one hand, leads to the variety of HO-AF semantics. However, on the other hand, it also brings difficulties in the formal examination of the relationship among these distinct semantics and the exploration of new semantics. This work aims at exploring the underlying commons in existing HO-AF semantics and examining the role of such commons in the study of HO-AF semantics. To this end, we introduce novel notion of renovation sets to represent the acceptability of attacks, and identify several types of renovation sets for different kinds of acceptability. We then use renovation sets to represent and compare HO-AF semantics built in four different approaches. The proposal of renovation sets provides a general tool for the study of HO-AFs. The representation demonstrates a method of using renovation sets to establish HO-AF semantics. The comparison shows the feasibility of investigating the relationship between different HO-AF semantics with the help of renovation sets.
