Bipolar Argumentation Frameworks Research Articles

Bipolar Argumentation Frameworks with a dual relation between defeat and defence

Abstract Bipolar Argumentation Frameworks ($\textit{BAF}$s) extend Dung’s Abstract Argumentation Frameworks ($\textit{AAF}$s) by incorporating an explicit notion of support between arguments. However, there is a price to pay: the semantics for $\textit{BAF}$s often involve more intricate definitions and computational procedures than those for $\textit{AAF}$s. In this paper, we establish a dual relation between defeat and defence. Taking profit from this dual perspective, we define conflict-free sets, acceptability, extension-based and labelling-based semantics as in $\textit{AAF}$s. We also show that our definitions collapse into the corresponding concepts proposed for $\textit{AAF}$s when the support relation is ignored. In particular, we prove the semantics $\beta $-admissible, $\beta $-complete, $\beta $-grounded, $\beta $-preferred, $\beta $-stable and $\beta $-semi-stable defined here for $\textit{BAF}$s are generalisations of the corresponding semantics for $\textit{AAF}$s. Besides generalising $\textit{AAF}$s semantics to $\textit{BAF}$s, our approach also preserves some of their most remarkable results, including Dung’s Fundamental Lemma.

Change in quantitative bipolar argumentation: Sufficient, necessary, and counterfactual explanations

This paper presents a formal approach to explaining change of inference in Quantitative Bipolar Argumentation Frameworks (QBAFs). When drawing conclusions from a QBAF and updating the QBAF to then again draw conclusions (and so on), our approach traces changes – which we call strength inconsistencies – in the partial order over argument strengths that a semantics establishes on some arguments of interest, called topic arguments. We trace the causes of strength inconsistencies to specific arguments, which then serve as explanations. We identify sufficient, necessary, and counterfactual explanations for strength inconsistencies and show that strength inconsistency explanations exist if and only if an update leads to strength inconsistency. We define a heuristic-based approach to facilitate the search for strength inconsistency explanations, for which we also provide an implementation.

Argumentative explanations for pattern-based text classifiers

Recent works in Explainable AI mostly address the transparency issue of black-box models or create explanations for any kind of models (i.e., they are model-agnostic), while leaving explanations of interpretable models largely underexplored. In this paper, we fill this gap by focusing on explanations for a specific interpretable model, namely pattern-based logistic regression (PLR) for binary text classification. We do so because, albeit interpretable, PLR is challenging when it comes to explanations. In particular, we found that a standard way to extract explanations from this model does not consider relations among the features, making the explanations hardly plausible to humans. Hence, we propose AXPLR, a novel explanation method using (forms of) computational argumentation to generate explanations (for outputs computed by PLR) which unearth model agreements and disagreements among the features. Specifically, we use computational argumentation as follows: we see features (patterns) in PLR as arguments in a form of quantified bipolar argumentation frameworks (QBAFs) and extract attacks and supports between arguments based on specificity of the arguments; we understand logistic regression as a gradual semantics for these QBAFs, used to determine the arguments’ dialectic strength; and we study standard properties of gradual semantics for QBAFs in the context of our argumentative re-interpretation of PLR, sanctioning its suitability for explanatory purposes. We then show how to extract intuitive explanations (for outputs computed by PLR) from the constructed QBAFs. Finally, we conduct an empirical evaluation and two experiments in the context of human-AI collaboration to demonstrate the advantages of our resulting AXPLR method.

Probabilistic causal bipolar abstract argumentation: an approach based on credal networks

The Bipolar Argumentation Framework approach is an extension of the Argumentation Framework. A Bipolar Argumentation Framework considers a support interaction between arguments, besides the attack interaction. As in the Argumentation Framework, some researches consider that arguments have a degree of uncertainty, which impacts on the degree of uncertainty of the extensions obtained from a Bipolar Argumentation Framework under a semantics. In these approaches, both the uncertainty of the arguments and of the extensions are modeled by means of precise probability values. However, in many real application domains there is a need for aggregating probability values from different sources so it is not suitable to aggregate such probability values in a unique probability distribution. To tackle this challenge, we use credal networks theory for modelling the uncertainty of the degree of belief of arguments in a BAF. We also propose an algorithm for calculating the degree of uncertainty of the extensions inferred by a given argumentation semantics. Moreover, we introduce the idea of modelling the support relation as a causal relation. We formally show that the introduced approach is sound and complete w.r.t the credal networks theory.

Handling support cycles and collective interactions in the logical encoding of higher-order bipolar argumentation frameworks

AbstractIn our paper [17], we have proposed a logical encoding of argumentation frameworks with higher-order interactions (i.e. attacks or supports whose targets are arguments or other attacks or supports) with an evidential interpretation for supports, such frameworks are called REBAF. With this encoding, we are able to characterize the semantics of REBAF under the form of specific logical models. Nevertheless, this encoding has two important drawbacks: first, the handling of support cycles has some weaknesses, and second, the collective interactions (i.e. attacks or supports whose source is a set of arguments and not only one argument) are not taken into account. The present paper proposes an improvement of this encoding for solving these drawbacks. Moreover, we also extend this proposal to another kind of Higher-Order Bipolar Argumentation Framework, the RAFN, in which supports are interpreted as necessity.

Argumentation-Based Online Incremental Learning

The environment around general-purpose service robots has a dynamic nature. Accordingly, even the robot’s programmer cannot predict all the possible external failures which the robot may confront. This research proposes an online incremental learning method that can be further used to autonomously handle external failures originating from a change in the environment. Existing research typically offers special-purpose solutions. Furthermore, the current incremental online learning algorithms cannot generalize well with just a few observations. In contrast, our method extracts a set of hypotheses, which can then be used for finding the best recovery behavior at each failure state. The proposed argumentation-based online incremental learning approach uses an abstract and bipolar argumentation framework to extract the most relevant hypotheses and model the defeasibility relation between them. This leads to a novel online incremental learning approach that overcomes the addressed problems and can be used in different domains including robotic applications. We have compared our proposed approach with state-of-the-art online incremental learning approaches, an approximation-based reinforcement learning method, and several online contextual bandit algorithms. The experimental results show that our approach learns more quickly with a lower number of observations and also has higher final precision than the other methods. Note to Practitioners—This work proposes an online incremental learning method that learns faster by using a lower number of failure states than other state-of-the-art approaches. The resulting technique also has higher final learning precision than other methods. Argumentation-based online incremental learning generates an explainable set of rules which can be further used for human-robot interaction. Moreover, testing the proposed method using a publicly available dataset suggests wider applicability of the proposed incremental learning method outside the robotics field wherever an online incremental learner is required. The limitation of the proposed method is that it aims for handling discrete feature values.

Interpreting and explaining pagerank through argumentation semantics

In this paper we show how re-interpreting PageRank as an argumentation semantics for a bipolar argumentation framework empowers its explainability. After showing that PageRank, naively re-interpreted as an argumentation semantics for support frameworks, fails to satisfy some generally desirable properties, we propose a novel approach able to reconstruct PageRank as a gradual semantics of a suitably defined bipolar argumentation framework, while satisfying these properties. We then show how the theoretical advantages afforded by this approach also enjoy an enhanced explanatory power: we propose several types of argument-based explanations for PageRank, each of which focuses on different aspects of the algorithm and uncovers information useful for the comprehension of its results.

Collective Argumentation: The Case of Aggregating Support-Relations of Bipolar Argumentation Frameworks

In many real-life situations that involve exchanges of arguments, individuals may differ on their assessment of which supports between the arguments are in fact justified, i.e., they put forward different support-relations. When confronted with such situations, we may wish to aggregate individuals' argumentation views on support-relations into a collective view, which is acceptable to the group. In this paper, we assume that under bipolar argumentation frameworks, individuals are equipped with a set of arguments and a set of attacks between arguments, but with possibly different support-relations. Using the methodology in social choice theory, we analyze what semantic properties of bipolar argumentation frameworks can be preserved by aggregation rules during the aggregation of support-relations.

Ranking comment sorting policies in online debates

Online debates typically possess a large number of argumentative comments. Most readers who would like to see which comments are winning arguments often only read a part of the debate. Many platforms that host such debates allow for the comments to be sorted, say from the earliest to latest. How can argumentation theory be used to evaluate the effectiveness of such policies of sorting comments, in terms of the actually winning arguments displayed to a reader who may not have read the whole debate? We devise a pipeline that captures an online debate tree as a bipolar argumentation framework (BAF), which is sorted depending on the policy, giving a sequence of induced sub-BAFs representing how and how much of the debate has been read. Each sub-BAF has its own set of winning arguments, which can be quantitatively compared to the set of winning arguments of the whole BAF. We apply this pipeline to evaluate policies on Kialo debates, where it is shown that reading comments from most to least liked, on average, displays more winners than reading comments earliest first. Therefore, in Kialo, reading comments from most to least liked is on average more effective than reading from the earliest to the most recent.

Combining Deep Learning and Argumentative Reasoning for the Analysis of Social Media Textual Content Using Small Data Sets

The use of social media has become a regular habit for many and has changed the way people interact with each other. In this article, we focus on analyzing whether news headlines support tweets and whether reviews are deceptive by analyzing the interaction or the influence that these texts have on the others, thus exploiting contextual information. Concretely, we define a deep learning method for relation–based argument mining to extract argumentative relations of attack and support. We then use this method for determining whether news articles support tweets, a useful task in fact-checking settings, where determining agreement toward a statement is a useful step toward determining its truthfulness. Furthermore, we use our method for extracting bipolar argumentation frameworks from reviews to help detect whether they are deceptive. We show experimentally that our method performs well in both settings. In particular, in the case of deception detection, our method contributes a novel argumentative feature that, when used in combination with other features in standard supervised classifiers, outperforms the latter even on small data sets.

Empirical evaluation of abstract argumentation: Supporting the need for bipolar and probabilistic approaches

In dialogical argumentation, it is often assumed that the involved parties will always correctly identify the intended statements posited by each other and realize all of the associated relations, conform to the three acceptability states (accepted, rejected, undecided), adjust their views whenever new and correct information comes in, and that a framework handling only attack relations is sufficient to represent their opinions. Although it is natural to make these assumptions as a starting point for further research, dropping some of them has become quite challenging.Probabilistic argumentation is one of the approaches that can be harnessed for more accurate user modelling. The epistemic approach allows us to represent how much a given argument is believed or disbelieved by a given person, offering us the possibility to express more than just three agreement states. It comes equipped with a wide range of postulates, including those that do not make any restrictions concerning how initial arguments should be viewed. Thus, this approach is potentially more suitable for handling beliefs of the people that have not fully disclosed their opinions or counterarguments with respect to standard Dung's semantics. The constellation approach can be used to represent the views of different people concerning the structure of the framework we are dealing with, including situations in which not all relations are acknowledged or when they are seen differently than intended. Finally, bipolar argumentation frameworks can be used to express both positive and negative relations between arguments.In this paper we will describe the results of an experiment in which participants were asked to judge dialogues in terms of agreement and structure. We will compare our findings with the aforementioned assumptions as well as with the constellation and epistemic approaches to probabilistic argumentation and bipolar argumentation.

Bipolarity in temporal argumentation frameworks

A Timed Argumentation Framework (TAF) is a formalism where arguments are only valid for consideration during specific intervals of time, called availability intervals, which are defined for every individual argument. The original proposal is based on a single abstract notion of attack between arguments that remains static and permanent in time. Thus, in general, when identifying the set of acceptable arguments, the outcome associated with a TAF will vary over time.Here, we are introducing an extension of TAF adding the capability of modeling a support relation between arguments. In this sense, the resulting framework provides a suitable model for different time-dependent issues; thus, the main contribution of this work is to provide an enhanced framework for modeling a positive (support) and negative (attack) interaction which varies over time, features that are highly relevant in many real-world situations. This addition leads to a Timed Bipolar Argumentation Framework (T-BAF), where classical argument extensions can be defined, aiming at advancing in the integration of temporal argumentation in different application domains.

Modelling defeasible and prioritized support in bipolar argumentation

Cayrol and Lagasquie-Schiex introduce bipolar argumentation frameworks by introducing a second relation on the arguments for representing the support among them. The main drawback of their approach is that they cannot encode defeasible support, for instance they cannot model an attack towards a support relation. In this paper, we introduce a way to model defeasible support in bipolar argumentation frameworks. We use the methodology of meta-argumentation in which Dung’s theory is used to reason about itself. Dung’s well-known admissibility semantics can be used on this meta-argumentation framework to compute the acceptable arguments, and all properties of Dung’s classical theory are preserved. Moreover, we show how different contexts can lead to the alternative strengthening of the support relation over the attack relation, and converse. Finally, we present two applications of our methodology for modeling support, the case of arguments provided with an internal structure and the case of abstract dialectical frameworks.

Extended Bipolar Argumentation Model

提出一种扩展双极辩论模型EBAF(extended bipolar argumentation framework).该模型不仅包括攻击和支援两种独立的语义关系,还允许攻击和支援的递归交互,即对攻击和支援关系进行攻击或支援,且递归次数不受限制.围绕该模型的可接受集合的确定问题,首先将该模型中的攻击和支援关系进行分离,得到攻击辩论框架和支援辩论框架;然后将攻击关系和支援关系作为实体,把递归攻击和递归支援转化为关系视角下的攻击和支援.在此基础上,定义了EBAF 的基本语义概念和可接受集合,并给出了可接受集合的确定算法.最后将EBAF 与其他相关辩论模型进行了比较.

Coalitions of arguments: A tool for handling bipolar argumentation frameworks

Bipolar argumentation frameworks enable to represent two kinds of interaction between arguments: support and conflict. In this paper, we turn a bipolar argumentation framework into a meta-argumentation framework where conflicts occur between sets of arguments, characterized as coalitions of supporting arguments. So, Dung's well-known semantics can be used on this meta-argumentation framework to select the acceptable arguments. © 2009 Wiley Periodicals, Inc.