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Handbook of Argumentation Theory pp 615–675Cite as

Argumentation and Artificial Intelligence

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Abstract

This chapter is devoted to contributions to the field of argumentation as developed in the field of artificial intelligence. In the last two decades, a community has been formed that addresses issues in argumentation theory focusing on methods and problems as studied in artificial intelligence. Much of this work is formal or computational in nature, but often has a relevance that goes beyond artificial intelligence per se. This chapter is an attempt to show this relevance to a wider audience by focusing on key ideas and themes and less on formal and computational detail. The chapter starts with historic roots of the treatment of argumentation in artificial intelligence, by discussing non-monotonic logic, in particular Raymond Reiter’s logic of default reasoning and logic programming, and defeasible reasoning, where especially John Pollock’s multifaceted treatment of argument defeat has shaped how argumentation is handled in artificial intelligence. The chapter continues with what is known in the field as abstract argumentation. In abstract argumentation, the focus of study is on attack between arguments, as an abstract formal relation, an approach proposed and developed by Phan Minh Dung. This approach has become very influential, but by its formal mathematical nature can prove daunting. Many key ideas can be explained in elementary terms, which is what we have aimed to do in Sect. 11.4. Then follows a discussion of artificial intelligence research into argument structure, with treatments of the role of argument specificity, conclusive force, the relation with classical logic, and the combination of support and attack. Next we treat argument schemes and argumentation dialogues, two areas of study where there is an especially strong cross-fertilization between argumentation theory and artificial intelligence. In part this can be explained by the study of argumentation by AI researchers focusing on the field of law and by the rise of the multi-agent systems perspective in computer science and artificial intelligence. Specific themes reviewed in the chapter are reasoning with rules and with cases, the role of the audience and values, argumentation support software, burden of proof and evidence, and argument strength. All in all we hope that the chapter helps to enhance the collaboration between artificial intelligence and argumentation theory.

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Notes

  1. 1.

    We mention a few of these journals: Artificial Intelligence, Artificial Intelligence and Law, Autonomous Agents and Multi-Agent Systems, Computational Intelligence, International Journal of Cooperative Information Systems, International Journal of Human-Computer Studies, Journal of Logic and Computation, and The Knowledge Engineering Review. Contributions have also been made to journals that deal primarily with argumentation, such as Argumentation and Informal Logic. A journal devoted explicitly to the interdisciplinary area of AI is Argument and Computation.

  2. 2.

    The first COMMA conference was held in Liverpool in 2006, followed by conferences in Toulouse (2008), Desenzano del Garda (2010), and Vienna (2012). See http://www.comma-conf.org/. ArgMAS (Argumentation in Multi-Agent Systems) and CMNA (Computational Models of Natural Argument) are related workshops.

  3. 3.

    Nine of the top twenty best cited articles in Artificial Intelligence since 2007 deal with argumentation, five of the top ten, and three of the top five. Source: Scopus.com, June 2012.

  4. 4.

    For a survey of the literature up till approximately 2002, we refer to the road map by Reed and Norman (2004a) and the more formally oriented overview by Prakken and Vreeswijk (2002). For more detail, including formal and computational elaboration, the interested reader may wish to consult the original sources referred to in this chapter. In addition, we refer to the collection of papers edited by Rahwan and Simari (Eds., 2009), which contains contributions by a great many researchers in the field of argumentation and artificial intelligence, and to the sources we mentioned in Notes 1 and 2. See also the special issue of the Artificial Intelligence journal edited by Bench-Capon and Dunne (2007).

  5. 5.

    See the entry on nonmonotonic logic in the Stanford Encyclopedia of Philosophy at http://plato.stanford.edu/entries/logic-nonmonotonic/ (Antonelli 2010).

  6. 6.

    See the opening sentence of the paper’s abstract: “What philosophers call defeasible reasoning is roughly the same as non-monotonic reasoning in AI” (Pollock 1987, p. 481).

  7. 7.

    In this volume, logical symbols are introduced in Sect. 3.3.5 and in Sect. 6.2.3. The symbol “∧” stands for conjunction (“and”).

  8. 8.

    Pollock aims for a theory of projectible properties. See also Pollock (1995, p. 66f).

  9. 9.

    See Hitchcock (2001, 2002a) for a survey and a discussion of the OSCAR project for those interested in argumentation. Hitchcock also gives further information about Pollock’s work on practical reasoning, i.e., reasoning concerning what to do.

  10. 10.

    This form of defeat is the basis of Bondarenko et al. (1997). We shall here not elaborate on the distinction between premises and assumptions. One way of thinking about assumptions is to see them as defeasible premises. See Sect. 11.5.3.

  11. 11.

    Prakken (2010) speaks of ways of attack, where argument defeat is the result of argument attack.

  12. 12.

    The ASPIC project (full name: Argumentation Service Platform with Integrated Components) was supported by the EU 6th Framework Programme and ran from January 2004 to September 2007. In the project, academic and industry partners cooperated in developing argumentation-based software systems.

  13. 13.

    The success of the paper is illustrated by its number of citations. By an imperfect but informative count in Google Scholar of July 22, 2013, there were 1938 citations.

  14. 14.

    This is especially helpful when also supporting connections are considered; see Sect. 11.5.

  15. 15.

    In the following, we make use of terminology proposed by Verheij (2007).

  16. 16.

    In establishing the concept, Verheij (1996b) used the term admissible stage extensions. The now standard term semi-stable extension was proposed by Caminada (2006).

  17. 17.

    Dung’s own definition of grounded extension, which does not use labelling, is not discussed here.

  18. 18.

    He believes that a projectibility constraint is required (1995, pp. 105–106). See Note 8.

  19. 19.

    Some would object to the use of the term rules here. Rules are here thought of in analogy with the inference rules of classical logic. An issue is then that, as such, they are not expressed in the logical object language, but in a metalanguage. In the context of defeasible reasoning and argumentation (and also in non-monotonic logic), this distinction becomes less clear. Often there is one logical language to express ordinary sentences, a second formal language (with less structure and/or less semantics and therefore not usually referred to as “logical”) used to express the rules, and the actual metalanguage that is used to define the formal system.

  20. 20.

    Although the term schème argumentative [argumentative scheme] was already used by Perelman and Olbrechts-Tyteca, according to Garssen (2001), van Eemeren et al. (1978, 1984) used the notion of argument(ation) scheme for the first time in its present sense. See also van Eemeren and Kruiger (1987), van Eemeren and Grootendorst (1992a), Kienpointner (1992), and Walton et al. (2008).

  21. 21.

    http://en.wikipedia.org/wiki/Nomic. See also Hofstadter (1996, chapter 4).

  22. 22.

    See also the study of Nomic by Vreeswijk (1995a).

  23. 23.

    For an overview of the field of multi-agent systems, see the textbook by Wooldridge (2009), which contains a chapter entitled “Arguing.”

  24. 24.

    The 2000 Symposium on Argument and Computation at Bonskeid House, Perthshire, Scotland, organized by Reed and Norman, has been a causal factor. See Reed and Norman (2004b).

  25. 25.

    A systematic overview of argumentation dialogue models of negotiation has been provided by Rahwan et al. (2003).

  26. 26.

    The primary journal of the field of AI and Law is Artificial Intelligence and Law, with the biennial ICAIL and annual JURIX as the main conferences.

  27. 27.

    The book is based on Prakken’s (1993) doctoral dissertation.

  28. 28.

    “∀x …” stands for “for every entity x it holds that ….” Similarly, for “∀y ….” See also Sect. 6.2 of this volume.

  29. 29.

    Reason-based logic exists in a series of versions, some introduced in collaboration with Verheij (e.g., Verheij 1996a).

  30. 30.

    We shall simplify Hage’s formalism a bit by omitting the explicit distinction between rules and principles.

  31. 31.

    See also Rissland and Ashley (1987), Ashley (1989), and Rissland and Ashley (2002).

  32. 32.

    The example is inspired by the case material used by Roth (2003).

  33. 33.

    In AI and law, the importance of the modelling of the values and goals underlying legal decisions was already acknowledged by Berman and Hafner (1993).

  34. 34.

    The book’s subtitle adds modestly: A Prolegomenon.

  35. 35.

    The reviews by Kirschner et al. (2003), Verheij (2005b), and Scheuer et al. (2010) provide further detail about argumentation support software.

  36. 36.

    http://rationale.austhink.com/

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Authors and Affiliations

  1. University of Amsterdam, Amsterdam, The Netherlands

    Frans H. van Eemeren, Bart Garssen, A. Francisca Snoeck Henkemans & Jean H. M. Wagemans

  2. University of Groningen, Groningen, The Netherlands

    Erik C. W. Krabbe

  3. Faculty of Mathematics and Natural Sciences, University of Groningen, Groningen, The Netherlands

    Bart Verheij

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van Eemeren, F.H., Garssen, B., Krabbe, E.C.W., Henkemans, A.F.S., Verheij, B., Wagemans, J.H.M. (2014). Argumentation and Artificial Intelligence. In: Handbook of Argumentation Theory. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-9473-5_11

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