Argumentation Framework Research Articles

Abstract argumentation with conditional preferences

In this paper, we study conditional preferences in abstract argumentation by introducing a new generalization of Dung-style argumentation frameworks (AFs) called Conditional Preference-based AFs (CPAFs). Each subset of arguments in a CPAF can be associated with its own preference relation. This generalizes existing approaches for preference-handling in abstract argumentation, and allows us to reason about conditional preferences in a general way. We conduct a principle-based analysis of CPAFs and compare them to related generalizations of AFs. Specifically, we highlight similarities and differences to Modgil’s Extended AFs and show that our formalism can capture Value-based AFs. Moreover, we show that in some cases the introduction of conditional preferences leads to an increase in computational complexity.

Learning argumentation frameworks from labelings

We consider the problem of learning argumentation frameworks from a given set of labelings such that every input is a σ-labeling of these argumentation frameworks. Our new algorithm takes labelings and computes attack constraints for each argument that represent the restrictions on argumentation frameworks that are consistent with the input labelings. Having constraints on the level of arguments allows for a very effective parallelization of all computations. An important element of this approach is maintaining a representation of all argumentation frameworks that satisfy the input labelings instead of simply finding any suitable argumentation framework. This is especially important, for example, if we receive additional labelings at a later time and want to refine our result without having to start all over again. The developed algorithm is compared to previous works and an evaluation of its performance has been conducted.

Equivalence in Argumentation Frameworks with a Claim-centric View: Classical Results with Novel Ingredients

A common feature of non-monotonic logics is that the classical notion of equivalence does not preserve the intended meaning in light of additional information. Consequently, the term strong equivalence was coined in the literature and thoroughly investigated. In the present paper, the knowledge representation formalism under consideration is claimaugmented argumentation frameworks (CAFs) which provide a formal basis to analyze conclusion-oriented problems in argumentation by adapting a claim-focused perspective. CAFs extend Dung AFs by associating a claim to each argument representing its conclusion. In this paper, we investigate both ordinary and strong equivalence in CAFs. Thereby, we take the fact into account that one might either be interested in the actual arguments or their claims only. The former point of view naturally yields an extension of strong equivalence for AFs to the claim-based setting while the latter gives rise to a novel equivalence notion which is genuine for CAFs. We tailor, examine and compare these notions and obtain a comprehensive study of this matter for CAFs. We conclude by investigating the computational complexity of naturally arising decision problems.

On the need to explicitize the unstated argument in cancer research: an ethnography on scientific argumentation

Within the framework of scientific argumentation, this study explores the role of what we here call the unstated argument in knowledge construction. The case study conducted in a cancer research lab in Brussels, uses observation, open interviews, and discourse analysis. Guided by Discursive Psychology as a theory and method, it examines the bases of a specific unstated argument embedded in tacit knowledge. The unstated argument is about medium usage in cancer research. The medium is a chemical liquid composed of a number of substances injected into the cells that scientists use to carry out experiments. The findings suggest that the unstated argument comprises a claim and de facto evidence. The claim is that the medium usage is appropriate and not problematic for research results. The evidence: (1) does not emanate from research; (2) is based on personal opinion; and (3) is backed up by the following factors: (a) practicing the status quo; (b) adhering to cancer-research standards; and (c) being bound by the demand and supply interplay. Provoked by the present study, counterevidence is ultimately substantiated by the same scientists. The counterevidence happens to challenge the claim, as it is based on expert-opinion. The study suggests that ethnography can offer a unique methodological stance to discern the unstated arguments embedded in tacit knowledge.

An Interleaving Semantics of the Timed Concurrent Language for Argumentation to Model Debates and Dialogue Games

AbstractTime is a crucial factor in modelling dynamic behaviours of intelligent agents: activities have a determined temporal duration in a real-world environment, and previous actions influence agents’ behaviour. In this paper, we propose a language for modelling concurrent interaction between agents that also allows the specification of temporal intervals in which particular actions occur. Such a language exploits a timed version of Abstract Argumentation Frameworks to realise a shared memory used by the agents to communicate and reason on the acceptability of their beliefs with respect to a given time interval. An interleaving model on a single processor is used for basic computation steps, with maximum parallelism for time elapsing. Following this approach, only one of the enabled agents is executed at each moment. To demonstrate the capabilities of the language, we also show how it can be used to model interactions such as debates and dialogue games taking place between intelligent agents. Lastly, we present an implementation of the language that can be accessed via a web interface.

On Dynamics in Structured Argumentation Formalisms

This paper is a contribution to the research on dynamics in assumption-based argumentation (ABA). We investigate situations where a given knowledge base undergoes certain changes. We show that two frequently investigated problems, namely enforcement of a given target atom and deciding strong equivalence of two given ABA frameworks, are intractable in general. Notably, these problems are both tractable for abstract argumentation frameworks (AFs) which admit a close correspondence to ABA by constructing semanticspreserving instances. Inspired by this observation, we search for tractable fragments for ABA frameworks by means of the instantiated AFs. We argue that the usual instantiation procedure is not suitable for the investigation of dynamic scenarios since too much information is lost when constructing the abstract framework. We thus consider an extension of AFs, called cvAFs, equipping arguments with conclusions and vulnerabilities in order to better anticipate their role after the underlying knowledge base is extended. We investigate enforcement and strong equivalence for cvAFs and present syntactic conditions to decide them. We show that the correspondence between cvAFs and ABA frameworks is close enough to capture dynamics in ABA. This yields the desired tractable fragment. We furthermore discuss consequences for the corresponding problems for logic programs.

Explainable acceptance in probabilistic and incomplete abstract argumentation frameworks

Dung's Argumentation Framework (AF) has been extended in several directions, including the possibility of representing uncertainty about the existence of arguments and attacks. In this regard, two main proposals have been introduced in the literature: Probabilistic Argumentation Framework (PrAF) and Incomplete Argumentation Framework (iAF). PrAF is an extension of AF with probability theory, thus representing quantified uncertainty. In contrast, iAF represents unquantified uncertainty, that is it can be seen as a special case where we only know that some elements (arguments or attacks) are uncertain. In this paper, we first address the problem of computing the probability that a given argument is accepted in PrAF. This is carried out by introducing the concept of probabilistic explanation for any given (probabilistic) extension. We show that the complexity of the problem is FP#P-hard and propose polynomial approximation algorithms with bounded additive error for PrAFs where odd-length cycles are forbidden. We investigate the approximate complexity of the related FP#P-hard problems of credulous and skeptical acceptance in PrAF, showing that they are generally harder than the problem of computing the probability that a given argument is accepted. Next we consider iAF and, after showing some equivalence properties among classes of iAFs, we study iAF as a special case of PrAF where uncertain elements have associated a probability equal to 1/2. Finally, given this result, we investigate the relationships between iAF acceptance problems and probabilistic acceptance in PrAF.

Abstract Argumentation Framework with Conditional Preferences

Dung's abstract Argumentation Framework (AF) has emerged as a central formalism in the area of knowledge representation and reasoning. Preferences in AF allow to represent the comparative strength of arguments in a simple yet expressive way. Preference-based AF (PAF) has been proposed to extend AF with preferences of the form a > b, whose intuitive meaning is that argument a is better than b. In this paper we generalize PAF by introducing conditional preferences of the form a > b \leftarrow body that informally state that a is better than b whenever the condition expressed by body is true. The resulting framework, namely Conditional Preference-based AF (CPAF), extends the PAF semantics under three well-known preference criteria, i.e. democratic, elitist, and KTV. After introducing CPAF, we study the complexity of the verification problem (deciding whether a set of arguments is a ``best'' extension) as well as of the credulous and skeptical acceptance problems (deciding whether a given argument belongs to any or all ``best'' extensions, respectively) under multiple-status semantics (that is, complete, preferred, stable, and semi-stable semantics) for the above-mentioned preference criteria.

The Effect of Preferences in Abstract Argumentation under a Claim-Centric View

In this paper, we study the effect of preferences in abstract argumentation under a claim-centric perspective. Recent work has revealed that semantical and computational properties can change when reasoning is performed on claim-level rather than on the argument-level, while under certain natural restrictions (arguments with the same claims have the same outgoing attacks) these properties are conserved. We now investigate these effects when, in addition, preferences have to be taken into account and consider four prominent reductions to handle preferences between arguments. As we shall see, these reductions give rise to different classes of claim-augmented argumentation frameworks, and behave differently in terms of semantic properties and computational complexity. This strengthens the view that the actual choice for handling preferences has to be taken with care.

On Undisputed Sets in Abstract Argumentation

We introduce the notion of an undisputed set for abstract argumentation frameworks, which is a conflict-free set of arguments, such that its reduct contains no non-empty admissible set. We show that undisputed sets, and the stronger notion of strongly undisputed sets, provide a meaningful approach to weaken admissibility and deal with the problem of attacks from self-attacking arguments, in a similar manner as the recently introduced notion of weak admissibility. We investigate the properties of our new semantical notions and show certain relationships to classical semantics, in particular that undisputed sets are a generalisation of preferred extensions and strongly undisputed sets are a generalisation of stable extensions. We also investigate the computational complexity of standard reasoning tasks with these new notions and show that they lie on the second and third level of the polynomial hierarchy, respectively.

A postulate-driven study of logical argumentation

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.

Persuasion-enhanced computational argumentative reasoning through argumentation-based persuasive frameworks

AbstractOne of the greatest challenges of computational argumentation research consists of creating persuasive strategies that can effectively influence the behaviour of a human user. From the human perspective, argumentation represents one of the most effective ways to reason and to persuade other parties. Furthermore, it is very common that humans adapt their discourse depending on the audience in order to be more persuasive. Thus, it is of utmost importance to take into account user modelling features for personalising the interactions with human users. Through computational argumentation, we can not only devise the optimal solution, but also provide the rationale for it. However, synergies between computational argumentative reasoning and computational persuasion have not been researched in depth. In this paper, we propose a new formal framework aimed at improving the persuasiveness of arguments resulting from the computational argumentative reasoning process. For that purpose, our approach relies on an underlying abstract argumentation framework to implement this reasoning and extends it with persuasive features. Thus, we combine a set of user modelling and linguistic features through the use of a persuasive function in order to instantiate abstract arguments following a user-specific persuasive policy. From the results observed in our experiments, we can conclude that the framework proposed in this work improves the persuasiveness of argument-based computational systems. Furthermore, we have also been able to determine that human users place a high level of trust in decision support systems when they are persuaded using arguments and when the reasons behind the suggestion to modify their behaviour are provided.

Capabilities approach and the marxist interpretation of the political conception of justice. reflections on the after-war restoration of Ukraine

Marxism as a normative position is critical of liberalism. However, the problems of justice and alienation that Marxism draws attention to can be solved by liberalism without the implementation of a Marxist political project. The purpose of the article is to substantiate the thesis that Martha Nussbaum's capabilities approach (one of the versions of political liberalism) is a more inclusive and rational method of theorizing about the basic principles of justice than Marxism. By analyzing Elizabeth Anderson's theory of liberal egalitarianism "democratic equality", I show that some of Marxist approaches that (1) do not deny the existence of justice and (2) are concerned with the problem of alienation, can apply the arsenal of the moral theory of political liberalism to solve these problems. I also criticize Rawls's and Nussbaum's theories of political liberalism for their procedural vagueness and non-inclusive theory of justice. I present an outline of my position, which I propose to call «extended political liberalism» (hereafter EPL). EPL is currently represented by two arguments: 1) the paideia argument and 2) the argument of the interdependence of the good and the right. The first allows talking about the actual implementation of basic rights and freedoms only when citizens are aware of them and have the opportunity to change their views regarding the personal conception of the good. This is impossible without qualitative basic education. Also, one of the central concepts of EPL is presented within the framework of Paideia's argument - the interpretation of the political conception of justice. Because of this concept, it makes sense to talk about the possibility of citizens' reflection on the reconciliation of the current political conception with the personal conception of the good. This can also be done through the analysis of other conceptions of the good. The second argument is a continuation of the first, appearing as a theoretical mechanism that allows supplementing the existing political conception in case of serious social changes. In conclusion, if we take into account the advantages of the capabilities approach and my criticism of its weaknesses, then a theoretical position will emerge that is quite acceptable to some representatives of Marxism. Moreover, the latter will not have to give up their basic beliefs. The peculiarity of my concept presented in this text is that it is not so much a critique of Marxism as an analysis of the basic normative judgments of political liberalism, with which some Marxists can agree. With this article, I also want to illustrate the strengths of such a concept and point to its prospects for further attempts to interpret the political conception by other normative positions.

On the study of acceptability in weighted argumentation frameworks through four-state labelling semantics

Abstract Computational Argumentation provides tools for both modelling and reasoning with controversial information. Abstract Argumentation Frameworks represent the building blocks in this field and allow one to model the relationships between arguments with the ultimate goal of establishing their acceptability. Arguments can be evaluated through sets of criteria, called semantics, which distinguish among various justification states. For example, an argument may be accepted, rejected, ignored or even marked as undecided. This paper considers Weighted Argumentation Frameworks and proposes a novel labelling semantics that differentiates four states and generalizes existing approaches.

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.

SmProbLog: Stable Model Semantics in ProbLog for Probabilistic Argumentation

AbstractArgumentation problems are concerned with determining the acceptability of a set of arguments from their relational structure. When the available information is uncertain, probabilistic argumentation frameworks provide modeling tools to account for it. The first contribution of this paper is a novel interpretation of probabilistic argumentation frameworks as probabilistic logic programs. Probabilistic logic programs are logic programs in which some of the facts are annotated with probabilities. We show that the programs representing probabilistic argumentation frameworks do not satisfy a common assumption in probabilistic logic programming (PLP) semantics, which is, that probabilistic facts fully capture the uncertainty in the domain under investigation. The second contribution of this paper is then a novel PLP semantics for programs where a choice of probabilistic facts does not uniquely determine the truth assignment of the logical atoms. The third contribution of this paper is the implementation of a PLP system supporting this semantics: smProbLog. smProbLog is a novel PLP framework based on the PLP language ProbLog. smProbLog supports many inference and learning tasks typical of PLP, which, together with our first contribution, provide novel reasoning tools for probabilistic argumentation. We evaluate our approach with experiments analyzing the computational cost of the proposed algorithms and their application to a dataset of argumentation problems.

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.

Integrated preference argumentation and applications in consumer behaviour analyses

Consumer preference studies in economics rest heavily on the behavioural interpretation of preference especially in the form of Revealed Preference Theory (RPT). Viewing purchasing decisions as a kind of human reasoning, in this paper we are interested in generalising behaviourism to preference-based argumentation where existing frameworks are arguably governed by the opposing mentalistic interpretation of preference. Concretely our contributions are three-fold. First we re-construct and unify two main approaches to RPT in argumentation terms by developing a Revealed Preference Argumentation (RPA) framework. We show that key RPT-based consumer analyses such as different rationality checks of a consumer behaviour and various kinds of such behaviour's extrapolations can be translated to computational tasks in RPA. RPA is in turn subsumed by the second contribution - an Integrated Preference Argumentation (IPA) framework which fully integrates mentalism and behaviourism. In particular we show that RPA is just a special case of IPA with only revealed preference, while existing preference-based argumentation frameworks can be viewed as IPA frameworks with only stated preference. As the third contribution, we develop a complete set of IPA algorithms and establish their correctness and termination for a general class of IPA frameworks. For demonstration we implement the algorithms in Prolog to obtain an IPA reasoning engine and test the engine with various RPT-based consumer behaviour analyses. In sum, it is argued that the current paper provides not only a theory of Integrated Preference Argumentation, but also a related development tool for future applications of argumentation to behavioural economics - a largely unexplored area so far though Dung used the stable marriage problem in microeconomic game theory to partly motivate the argumentation theory.

Towards a Temporal Probabilistic Argumentation Framework

In recent years, the notion of time has been studied in different ways in Dung-style Argumentation Frameworks. For example, time intervals of availability have been added to arguments and relations. As a result, the output of Dung semantics varies over time. In this paper, we consider the situation in which arguments hold with a certain probability distribution during a given interval. To model the uncertain character of events, we propose different notions of temporal conflict between arguments according to the type of availabilities intersection (partial, inclusive, or total). Then, we refine these notions of conflict by a defeat relation, using criterion functions that evaluate an attack’s significance according to the probability over time.

Correction to: Argumentation Frameworks with Attack Classification

Correction to: Argumentation Frameworks with Attack Classification