Value-based Argumentation Frameworks Research Articles

Using n-ary multi-modal logics in argumentation frameworks to reason about ethics

Autonomous behaviors may raise ethical issues that agents must consider in their reasoning. Some approaches use deontic logics, while others consider a value-based argumentation framework. However, no work combines both modal logic and argumentation to reason about ethics. Hence, we propose a new argumentation framework where arguments are built from a n-ary multi-modal logic. It allows to express different kinds of operators, e.g. nullary choice or moral worth operators, dyadic deontic operators or mental states. However the standard attacks based on logical contradictions are no longer sufficient to catch an intuitive meaning for attacks. Hence, we enrich standard attacks by characterizing how oppositions between modal operators arise. Furthermore we show the standard logic-based attacks have a quasi-symmetry property, i.e. when an argument attacks another, this argument is necessarily attacked by another one. Our modal attacks do not have this property, which is highly relevant to decide a dilemma.

Confronting value-based argumentation frameworks with people’s assessment of argument strength

We reported a series of experiments carried out to confront the underlying intuitions of value-based argumentation frameworks (VAFs) with the intuitions of ordinary people. Our goal was twofold. On the one hand, we intended to test VAF as a descriptive theory of human argument evaluations. On the other, we aimed to gain new insights from empirical data that could serve to improve VAF as a normative model. The experiments showed that people’s acceptance of arguments deviates from VAF’s semantics and is rather correlated with the importance given to the promoted values, independently of the perceptions of argument interactions through attacks and defeats. Furthermore, arguments were often perceived as promoting more than one value with different relative strengths. Individuals’ analyses of scenarios were also affected by external factors such as biases and arguments not explicit in the framework. Finally, we confirmed that objective acceptance, that is, the acceptance of arguments under any order of the values, was not a frequent behavior. Instead, participants tended to accept only the arguments that promoted the values they subscribe.

Formalizing value-guided argumentation for ethical systems design

The persuasiveness of an argument depends on the values promoted and demoted by the position defended. This idea, inspired by Perelman’s work on argumentation, has become a prominent theme in artificial intelligence research on argumentation since the work by Hafner and Berman on teleological reasoning in the law, and was further developed by Bench-Capon in his value-based argumentation frameworks. One theme in the study of value-guided argumentation is the comparison of values. Formal models involving value comparison typically use either qualitative or quantitative primitives. In this paper, techniques connecting qualitative and quantitative primitives recently developed for evidential argumentation are applied to value-guided argumentation. By developing the theoretical understanding of intelligent systems guided by embedded values, the paper is a step towards ethical systems design, much needed in these days of ever more pervasive AI techniques.

Value-based argumentation framework built from prioritized qualitative choice logic

Abstract The notion of preference is crucial in many fields. This justifies the development of many formalisms for preferences representation such as CP-nets, qualitative choice logic and its extensions. Preferences help to choose the best option in decision making, to compare between arguments in argumentation theory, etc. In this paper, we establish a link between a preference formalism, called Prioritized Qualitative Choice Logic (PQCL) and argumentation theory. We show that for any set of preferences expressed using PQCL (called PQCL theory), a Value-based Argumentation Framework (VAF) can be built. However, we point out some problems related to the evaluation of arguments which does not guarantee the correspondence between elements of PQCL theory and those of its associated VAF. We show that the major problem is due to the evaluation of arguments defined in existing argumentation frameworks, where an absolute status is assigned to each argument: objectively (or skeptically) accepted if it belongs to every extension, subjectively (or credulously) accepted if it is in some extensions and not in others and rejected if it does not belong to any extension. To deal with this problem, we propose to revise the evaluation of arguments in the corresponding VAF. As a result, there is a direct relationship between preferred extensions of the corresponding VAF and preferred models of a set of preferences expressed using PQCL. In addition, rank ordering the set of arguments is possible. The relationship between the two formalisms is interesting since on the one hand, it points out that one should be careful in using argumentation theory for decision making purposes or in formalizing a given problem as an argumentation framework and on the other hand, it makes it possible to use an argumentation framework for preference elicitation.

Algorithms for decision problems in argument systems under preferred semantics

For Dungʼs model of abstract argumentation under preferred semantics, argumentation frameworks may have several distinct preferred extensions: i.e., in informal terms, sets of acceptable arguments. Thus the acceptance problem (for a specific argument) can consider deciding whether an argument is in at least one such extensions (credulously accepted) or in all such extensions (skeptically accepted). We start by presenting a new algorithm that enumerates all preferred extensions. Following this we build algorithms that decide the acceptance problem without requiring explicit enumeration of all extensions. We analyze the performance of our algorithms by comparing these to existing ones, and present experimental evidence that the new algorithms are more efficient with respect to the expected running time. Moreover, we extend our techniques to solve decision problems in a widely studied development of Dungʼs model: namely value-based argumentation frameworks (vafs). In this regard, we examine analogous notions to the problem of enumerating preferred extensions and present algorithms that decide subjective, respectively objective, acceptance.

Parametric properties of ideal semantics

The concept of “ideal semantics” has been promoted as an alternative basis for skeptical reasoning within abstract argumentation settings. Informally, ideal acceptance not only requires an argument to be skeptically accepted in the traditional sense but further insists that the argument is in an admissible set all of whose arguments are also skeptically accepted. The original proposal was couched in terms of the so-called preferred semantics for abstract argumentation. We argue, in this paper, that the notion of “ideal acceptability” is applicable to arbitrary semantics and justify this claim by showing that standard properties of classical ideal semantics, e.g. unique status, continue to hold in any “reasonable” extension-based semantics. We categorise the relationship between the divers concepts of “ideal extension w.r.t. semantics σ” that arise and we present a comprehensive analysis of algorithmic and complexity-theoretic issues. In addition we offer further support for the view that “ideal semantics” ought to be seen as a generic property by presenting and analysing the forms that these might take within value-based argumentation frameworks.

Value-based Argumentation Frameworks as Neural-symbolic Learning Systems

While neural networks have been successfully used in a number of machine learning applications, logical languages have been the standard for the representation of argumentative reasoning. In this paper, we establish a relationship between neural networks and argumentation networks, combining reasoning and learning in the same argumentation framework. We do so by presenting a new neural argumentation algorithm, responsible for translating argumentation networks into standard neural networks. We then show a correspondence between the two networks. The algorithm works not only for acyclic argumentation networks, but also for circular networks, and it enables the accrual of arguments through learning as well as the parallel computation of arguments.

Persuasion in Practical Argument Using Value-based Argumentation Frameworks

In many cases of disagreement, particularly in situations involving practical reasoning, it is impossible to demonstrate conclusively that either party is wrong. The role of argument in such cases is to persuade rather than to prove, demonstrate or refute. Following Perelman, we argue that persuasion in such cases relies on a recognition that the strength of an argument depends on the social values that it advances, and that whether the attack of one argument on another succeeds depends on the comparative strength of the values advanced by the arguments concerned. To model this we extend the standard notion of Argumentation Frameworks (AFs) to Value-based Argumentation Frameworks (VAFs). After defining VAFs we explore their properties, and show how they can provide a rational basis for the acceptance or rejection of arguments, even where this would appear to be a matter of choice in a standard AF. In particular we show that in a VAF certain arguments can be shown to be acceptable however the relative strengths of the values involved are assessed. This means that disputants can concur on the acceptance of arguments, even when they differ as to which values are more important, and hence that we can identify points for which persuasion should be possible. We illustrate the above using an example moral debate. We then show how factual considerations can be admitted to our framework and discuss the possibility of persuasion in the face of uncertainty and disagreement as to values.

Agreeing to Differ: Modelling Persuasive Dialogue Between Parties With Different Values

In some cases of disagreement, particularly in ethics and law, it is impossible to provide any conclusive demonstration. The role of argument in such cases is to persuade rather than to prove. Drawing on ideas ofPerelrnan, we argue that persuasion
 in such cases relies on a recognition that the strength of such arguments will vary according to their audience, and depends on the comparative weight that the audiences gives to the social values that it advances. To model this, we introduce the notion of Value-based Argumentation Frameworks (VAFs),
 an extension of Argumentation Frameworks as originally introduced by Dung. We then describe a dialogue game based on VAFs, designed to model persuasive argumentation, which we illustrate with a widely discussed ethical problem.