Toward Artificial Argumentation
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Articles Toward Artifcial Argumentation Katie Atkinson, Pietro Baroni, Massimiliano Giacomin, Anthony Hunter, Henry Prakken, Chris Reed, Guillermo Simari, Matthias Thimm, Serena Villata ■ The feld of computational models of umans argue.1 This distinctive feature is at the same argument is emerging as an important time an important cognitive capacity and a powerful aspect of artifcial intelligence research. Hsocial phenomenon. It has attracted attention and The reason for this is based on the careful analysis since the dawn of civilization, being inti- recognition that if we are to develop mately related to the origin of any form of social organiza- robust intelligent systems, then it is tion, from political debates to law, and of structured think- imperative that they can handle incom- ing, from philosophy to science and arts. plete and inconsistent information in a way that somehow emulates the way As a cognitive capacity, argumentation is important for humans tackle such a complex task. handling conficting beliefs, assumptions, viewpoints, opin- And one of the key ways that humans ions, goals, and many other kinds of mental attitudes. When do this is to use argumentation either we are faced with a situation where we fnd that our infor- internally, by evaluating arguments and mation is incomplete or inconsistent, we often resort to the counterarguments‚ or externally, by for use of arguments in favor and against a given position in instance entering into a discussion or order to make sense of the situation. When we interact with debate where arguments are exchanged. other people we often exchange arguments in a cooperative As we report in this review, recent devel- or competitive fashion to reach a fnal agreement or to opments in the feld are leading to tech- nology for artifcial argumentation, in defend and promote an individual position. the legal, medical, and e-government domains, and interesting tools for argu- ment mining, for debating technologies, and for argumentation solvers are emerging. Copyright © 2017, Association for the Advancement of Artifcial Intelligence. All rights reserved. ISSN 0738-4602 FALL 2017 25 Articles Occurring continuously both in our mind and in and specifc formalisms may inextricably merge the social arena, argumentation pervades our intelli- together aspects relevant to different layers. gent behavior and the challenge of developing artif- cial argumentation systems appears to be as diverse Structural Layer and exciting as the challenge of artifcial intelligence This layer concerns the structure of the arguments itself. and how they are built: essentially it specifes, in a Indeed, this rich and important phenomenon given context, what an argument looks like, in terms offers an opportunity to develop models and tools for of its internal structure, and which are the ingredi- argumentation and to conceive autonomous artifcial ents for its construction. To exemplify, in contexts agents that can exploit these models and tools in the where arguments are built from a logical knowledge cognitive tasks they are required to carry out. To this base the ingredients are the logical formulae includ- purpose, a number of interesting lines of research are ed in the knowledge base. Then one way to build being investigated within artifcial intelligence and arguments is by simply applying the logic of the lan- several neighbor felds, leading to the establishment guage in which the knowledge base is stated to derive of computational models of argument as a promising conclusions. An argument here can be seen as a pair interdisciplinary research area. Progress in this area is ( , ) where is a subset of the knowledge base (a set expected to contribute to signifcant advances in the of formulae) that logically entails (a formula). Here, understanding and modeling of various aspects of Φisα called theΦ support, and is the claim, of the argu- human intelligence. ment. Other approaches considerα argument con- In this article, we review formalisms for capturing structionΦ from knowledge basesα as applying rules to various aspects of argumentation, and we present the formulas from the knowledge base, where the advances in their applications, with the aim to com- rules may be defeasible. In these rule-based approach- municate how research is making progress toward es an argument is typically seen as a tree whose root the goal of making artifcial argumentation tech- is the claim or conclusion, whose leaves are the nologies and systems a mature and widespread reali- premises on which the argument is based, and whose ty. In this brief review, we are unable to discuss or cite structure corresponds to the application of the rules all the relevant literature, and we suggest that the from the premises to the conclusion. Investigations interested reader seek more detailed coverage of the into the structural layer were initiated by Pollock foundations from Rahwan and Simari (2009), of (1992). Prominent examples of rule-based formalisms applications from Modgil et al. (2013), and of recent are ASPIC+, assumption-based argumentation (ABA), developments from the proceedings of the Interna- and defeasible logic programming (DeLP). For a tuto- tional Conference on Computational Models of rial introduction to formalisms for structured argu- Argument series,2 and the Argument and Computation mentation, see Besnard et al. (2014). journal.3 Arguments are not built from knowledge bases only, however. For instance, interactive systems that acquire arguments from users may adopt the Models of Argument approach of argumentation schemes (Walton, Reed, Computational models of argument are being devel- and Macagno 2008), namely stereotypical reasoning oped to refect aspects of how humans build, patterns, where in addition to the premises and the exchange, analyze, and use arguments in their daily claim, a set of critical questions is considered. Criti- lives to deal with a world where the information may cal questions provide a sort of checklist of issues that be controversial, incomplete, or inconsistent (Bench- can be raised to challenge arguments built on the Capon and Dunne 2007, Rahwan and Simari 2009). basis of a given scheme. Argumentation schemes The diversity of the manifestations of arguments in have also been used as a source of defeasible infer- real life implies diversity in the relevant models too ence rules in rule-based approaches to argument con- and the impossibility to reduce the vast available lit- struction from knowledge bases. In addition, argu- erature to a single reference scheme. It is possible mentation schemes are often considered in the however to identify some layers that can be regarded context of argument mining (see the Argument Min- as basic building blocks for the construction of an ing section) where the goal is to identify and extract argumentation model. Specifc modeling approaches the argumentative structures embedded in a natural may differ in the selection of which layers they actu- language source, providing a machine-processable ally use, in the way the selected layers are combined, representation of them. and in the formalization adopted within each layer. The variety of existing argument models raises the We consider the following fve main layers: struc- issue of exchanging or sharing arguments among dif- tural, relational, dialogical, assessment, and rhetori- ferent systems. This problem is addressed by the cal. They are described in the following and also sum- argument interchange format initiative (Chesñevar marized in Figure 1. Note that while each layer has its et al. 2006), aimed at providing an interlingua own nature and distinctive traits, the boundaries between various more concrete argumentation lan- between layers may not be so neat in some contexts, guages, on the basis of a generic abstract ontology. 26 AI MAGAZINE Articles Structural layer: How are arguments constructed? Relational layer: What are the relationships between arguments? Dialogical layer: How can argumentation be undertaken in dialogues? Assessment layer: How can a constellation of interacting arguments be evaluated and conclusions drawn? Rhetorical layer: How can argumentation be tailored for an audience so that it is persuasive? Figure 1. Key Aspects of Argumentation. Relational Layer strength to credibility to value-based evaluations. Arguments do not live in isolation and are linked to What relationships are signifcant and how to each other by various types of relations: the relation- identify them are highly context-dependent matters. al layer deals with identifying and formally repre- Note in particular that identifying argument rela- senting them, in view of their use in other layers or tions may be an easy mechanical procedure in set- even for descriptive and presentation purposes, since tings where arguments are formally built from a they are essential for an understanding of what is knowledge base, while in an argument mining sce- actually going on in an argumentation process. nario it is a task as challenging as the identifcation Examples of important relationships are (1) the sub- of the arguments themselves. argument and superargument relationships, indicat- Dialogical Layer ing how an argument is built incrementally on top of other arguments; (2) the attack relationship, indicat- This layer deals with the exchange of arguments ing that an argument is incompatible with another among different agents (or even between an agent argument in some sense, for example, because they and itself, in a scenario where