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The Added Value of Argumentation

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Agreement Technologies

Part of the book series: Law, Governance and Technology Series ((LGTS,volume 8))

Abstract

We discuss the value of argumentation in reaching agreements, based on its capability for dealing with conflicts and uncertainty. Logic-based models of argumentation have recently emerged as a key topic within Artificial Intelligence. Key reasons for the success of these models is that they are akin to human models of reasoning and debate, and their generalisation to frameworks for modelling dialogues. They therefore have the potential for bridging between human and machine reasoning in the presence of uncertainty and conflict. We provide an overview of a number of examples that bear witness to this potential, and that illustrate the added value of argumentation. These examples amount to methods and techniques for argumentation to aid machine reasoning (e.g. in the form of machine learning and belief functions) on the one hand and methods and techniques for argumentation to aid human reasoning (e.g. for various forms of decision making and deliberation and for the Web) on the other. We also identify a number of open challenges if this potential is to be realised, and in particular the need for benchmark libraries.

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Notes

  1. 1.

    As witnessed by the recently inaugurated series of international conferences and workshops (www.comma-conf.org, www.mit.edu/~irahwan/argmas/argmas11, www.csd.abdn.ac.uk/~niroren/TAFA-11) and major European research projects (ARGUGRID: www.argugrid.eu, ASPIC: www.cossac.org/projects/aspic, IMPACT: www.policy-impact.eu)

  2. 2.

    Our reviews in these sections are by no means comprehensive; rather, selected examples are chosen to illustrate the salient points.

  3. 3.

    Different parts of this chapter have been written/edited by different authors, as follows:

    • this Sect. 21.1 has been written by Sanjay Modgil;

    • Section 21.2 has been edited by Francesca Toni, with Sect. 21.2.1 written by Ivan Bratko and Martin Možina and Sect. 21.2.2 written by Francesca Toni;

    • Section 21.3 has been edited by Sanjay Modgil, with Sect. 21.3.1.1 written by Sanjay Modgil, Sect. 21.3.1.2 written by Carlos Chesñevar, Sect. 21.3.1.3 written by Francesca Toni, QUI Sect. 21.3.2.1 written by Sanjay Modgil, Sect. 21.3.2.2 written by Thomas Gordon, Sect. 21.3.2.3 written by Francesca Toni, Sect. 21.3.2.4 written by Xiuyi Fan and Francesca Toni, Sect. 21.3.3 written by Floris Bex, Chris Reed and Sanjay Modgil, and Sect. 21.3.4 written by Joao Leite and Paolo Torroni;

    • Section 21.4 has been written by Wolfgang Dvořák, Sarah Alice Gaggl, Stefan Szeider and Stefan Woltran;

    • Section 21.5 has been written by Sanjay Modgil and Francesca Toni.

  4. 4.

    Due to space limitations, we will only roughly describe ABML (see Možina et al. 2007 for precise details).

  5. 5.

    www.argugrid.eu

  6. 6.

    www.cossac.org/projects/aspic

  7. 7.

    www.policy-impact.eu

  8. 8.

    www.argugrid.eu

  9. 9.

    www.policy-impact.eu

  10. 10.

    Rules, assumptions, and contraries are components of ABA.

  11. 11.

    ova.computing.dundee.ac.uk

  12. 12.

    Meanwhile, some more insults appeared, which increased the comments counter.

  13. 13.

    A 2010 survey illustrates Facebook overtaking Google’s popularity among US Internet users. See “Facebook becomes bigger hit than Google” by By Chris Nuttall and David Gelles on Financial Times, online March 17, 2010 www.ft.com/cms/s/2/67e89ae8-30f7-11df-b057-00144feabdc0.html#axzz1MSvZe0pb. Recently Facebook is investing on a “social web search” project in order to better exploit its social data. See “Facebook Delves Deeper Into Search” By Douglas MacMillan and Brad Stone on Bloomberg Business Week, online March 29, 2012 www.businessweek.com/articles/2012-03-28/facebook-delves-deeper-into-search.

  14. 14.

    http://lidia.cs.uns.edu.ar/delp_client/

  15. 15.

    http://www.doc.ic.ac.uk/~ft/CaSAPI/

  16. 16.

    http://carneades.berlios.de/

  17. 17.

    http://www.arg.dundee.ac.uk/toast/

  18. 18.

    http://www.ai.rug.nl/~verheij/comparg/

  19. 19.

    http://heen.webfactional.com/

  20. 20.

    http://rull.dbai.tuwien.ac.at:8080/ASPARTIX

  21. 21.

    Answer-Set Programming (ASP) (Niemelä, 1999) is a declarative programming paradigm which allows for succinct representation of combinatorial problems.

  22. 22.

    Benchmarking of Qualitative Spatial and Temporal Reasoning Systems, Stanford University, CA, USA, March 23–25, 2009

  23. 23.

    www.cs.huji.ac.il/project/UAI10/fileFormat.php

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Acknowledgements

Many thanks to Jordi Sabater-Mir and Vicente Botti for useful suggestions and comments on an earlier version of this chapter.

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  1. Kings’ College London, London, UK

    Sanjay Modgil

  2. Imperial College London, London, UK

    Francesca Toni & Xiuyi Fan

  3. University of Dundee, Dundee, UK

    Floris Bex & Chris Reed

  4. University of Ljubljana, Ljubljana, Slovenia

    Ivan Bratko & Martin Možina

  5. Universidad Nacional del Sur, Bahía Blanca, Buenos Aires, Argentina

    Carlos I. Chesñevar, Marcelo A. Falappa, Alejandro J. García, María P. González & Guillermo R. Simari

  6. Vienna University of Technology, Vienna, Austria

    Wolfgang Dvořák, Sarah Alice Gaggl, Stefan Szeider & Stefan Woltran

  7. Fraunhofer FOKUS, Berlin, Germany

    Thomas F. Gordon

  8. Universidade Nova de Lisboa, Lisbon, Portugal

    João Leite

  9. University of Bologna, Bologna, Italy

    Paolo Torroni

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Modgil, S. et al. (2013). The Added Value of Argumentation. In: Ossowski, S. (eds) Agreement Technologies. Law, Governance and Technology Series, vol 8. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-5583-3_21

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