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Argumentation and Artificial Intelligence

  • Frans H. van Eemeren
  • Bart Garssen
  • Erik C. W. Krabbe
  • A. Francisca Snoeck Henkemans
  • Bart Verheij
  • Jean H. M. Wagemans
Reference work entry

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.

Key Terms

Abstract argumentation Admissible set of arguments Argumentation and artificial intelligence Argumentation dialogue Argument(ation) scheme Argumentation support software Argument evaluation Argument strength Argument structure Burden of proof Case-based reasoning Case study Default rule Defeasible reasoning Evidence Forms of argument defeat Inference to the best explanation Non-monotonic logic Rule-based reasoning with rules Values and audiences 

References

  1. Aleven, V. (1997). Teaching case-based reasoning through a model and examples. Doctoral dissertation, University of Pittsburgh.Google Scholar
  2. Aleven, V., & Ashley, K. D. (1997a). Evaluating a learning environment for case-based argumentation skills. In Proceedings of the sixth international conference on artificial intelligence and law (pp. 170–179). New York: ACM Press.Google Scholar
  3. Aleven, V., & Ashley, K. D. (1997b). Teaching case-based argumentation through a model and examples. Empirical evaluation of an intelligent learning environment. In B. du Boulay & R. Mizoguchi (Eds.), Artificial intelligence in education. Proceedings of AI-ED 97 world conference (pp. 87–94). Amsterdam: IOS Press.Google Scholar
  4. Alexy, R. (1978). Theorie der juristischen Argumentation [Theory of legal argumentation]. Frankfurt am Main: Suhrkamp Verlag.Google Scholar
  5. Amgoud, L. (2009). Argumentation for decision making. In I. Rahwan & G. R. Simari (Eds.), Argumentation in artificial intelligence (pp. 301–320). Dordrecht: Springer.Google Scholar
  6. Amgoud, L., Cayrol, C., Lagasquie-Schiex, M. C., & Livet, P. (2008). On bipolarity in argumentation frameworks. International Journal of Intelligent Systems, 23(10), 1062–1093.Google Scholar
  7. Antonelli, G. A. (2010). Non-monotonic logic. In E. N. Zalta (Ed.), The Stanford encyclopedia of philosophy. Summer 2010 ed. http://plato.stanford.edu/archives/sum2010/entries/logic-non-monotonic/
  8. Antoniou, G., Billington, D., Governatori, G., & Maher, M. (2001). Representation results for defeasible logic. ACM Transactions on Computational Logic, 2(2), 255–287.Google Scholar
  9. Ashley, K. D. (1989). Toward a computational theory of arguing with precedents. Accommodating multiple interpretations of cases. In Proceedings of the second international conference on artificial intelligence and law (pp. 93–102). New York: ACM Press.Google Scholar
  10. Ashley, K. D. (1990). Modeling legal argument. Reasoning with cases and hypotheticals. Cambridge, MA: The MIT Press.Google Scholar
  11. Atkinson, K. (2012). Introduction to special issue on modelling Popov v. Hayashi. Artificial Intelligence and Law, 20, 1–14.Google Scholar
  12. Atkinson, K., & Bench-Capon, T. J. M. (2007). Practical reasoning as presumptive argumentation using action based alternating transition systems. Artificial Intelligence, 171, 855–874.Google Scholar
  13. Atkinson, K., Bench-Capon, T. J. M., & McBurney, P. (2005). A dialogue game protocol for multi-agent argument over proposals for action. Autonomous Agents and Multi-Agent Systems, 11, 153–171.Google Scholar
  14. Atkinson, K., Bench-Capon, T. J. M., & McBurney, P. (2006). Computational representation of practical argument. Synthese, 152, 157–206.Google Scholar
  15. Baroni, P., Caminada, M., & Giacomin, M. (2011). An introduction to argumentation semantics. Knowledge Engineering Review, 26(4), 365–410.Google Scholar
  16. Barth, E. M., & Krabbe, E. C. W. (1982). From axiom to dialogue. A philosophical study of logics and argumentation. Berlin: de Gruyter.Google Scholar
  17. Bench-Capon, T. J. M. (2003). Persuasion in practical argument using value-based argumentation frameworks. Journal of Logic and Computation, 13(3), 429–448.Google Scholar
  18. Bench-Capon, T. J. M., Araszkiewicz, M., Ashley, K., Atkinson, K., Bex, F., Borges, F., Bourcier, D., Bourgine, D., Conrad, J. G., Francesconi, E., Gordon, T. F., Governatori, G., Leidner, J. L., Lewis, D. D., Loui, R. P., McCarty, L. T., Prakken, H., Schilder, F., Schweighofer, E., Thompson, P., Tyrrell, A., Verheij, B., Walton, D. N., & Wyner, A. Z. (2012). A history of AI and Law in 50 papers: 25 years of the international conference on AI and Law. Artificial Intelligence and Law, I, 20(3), 215–319.Google Scholar
  19. Bench-Capon, T. J. M., & Dunne, P. E. (2007). Argumentation in artificial intelligence. Artificial Intelligence, 171, 619–641.Google Scholar
  20. Bench-Capon, T. J. M., Freeman, J. B., Hohmann, H., & Prakken, H. (2004). Computational models, argumentation theories and legal practice. In C. A. Reed & T. J. Norman (Eds.), Argumentation machines. New frontiers in argument and computation (pp. 85–120). Dordrecht: Kluwer.Google Scholar
  21. Bench-Capon, T. J. M., Geldard, T., & Leng, P. H. (2000). A method for the computational modelling of dialectical argument with dialogue games. Artificial Intelligence and Law, 8, 233–254.Google Scholar
  22. Bench-Capon, T. J. M., Prakken, H., & Sartor, G. (2009). Argumentation in legal reasoning. In I. Rahwan & G. R. Simari (Eds.), Argumentation in artificial intelligence (pp. 363–382). Dordrecht: Springer.Google Scholar
  23. Bench-Capon, T. J. M., & Sartor, G. (2003). A model of legal reasoning with cases incorporating theories and values. Artificial Intelligence, 150, 97–143.Google Scholar
  24. Berman, D., & Hafner, C. (1993). Representing teleological structure in case-based legal reasoning. The missing link. In Proceedings of the fourth international conference on artificial intelligence and law (pp. 50–59). New York: ACM Press.Google Scholar
  25. Besnard, P., & Hunter, A. (2008). Elements of argumentation. Cambridge, MA: The MIT Press.Google Scholar
  26. Bex, F. J. (2011). Arguments, stories and criminal evidence. A formal hybrid theory. Dordrecht: Springer.Google Scholar
  27. Bex, F. J., van Koppen, P., Prakken, H., & Verheij, B. (2010). A hybrid formal theory of arguments, stories and criminal evidence. Artificial Intelligence and Law, 18(2), 123–152.Google Scholar
  28. Bex, F. J., Prakken, H., Reed, C., & Walton, D. N. (2003). Towards a formal account of reasoning about evidence. Argumentation schemes and generalisations. Artificial Intelligence and Law, 11, 125–165.Google Scholar
  29. Bex, F. J., & Verheij, B. (2012). Solving a murder case by asking critical questions. An approach to fact-finding in terms of argumentation and story schemes. Argumentation, 26(3), 325–353.Google Scholar
  30. Bondarenko, A., Dung, P. M., Kowalski, R. A., & Toni, F. (1997). An abstract, argumentation-theoretic approach to default reasoning. Artificial Intelligence, 93, 63–101.Google Scholar
  31. van den Braak, S. W., Vreeswijk, G., & Prakken, H. (2007). AVERs. An argument visualization tool for representing stories about evidence. In Proceedings of the 11th international conference on artificial intelligence and law (pp. 11–15). New York: ACM Press.Google Scholar
  32. Branting, L. K. (1991). Building explanations from rules and structured cases. International Journal of Man–Machine Studies, 34, 797–837.Google Scholar
  33. Branting, L. K. (2000). Reasoning with rules and precedents. A computational model of legal analysis. Dordrecht: Kluwer.Google Scholar
  34. Bratko, I. (2001). PROLOG. Programming for artificial intelligence (3rd ed.). Harlow: Pearson (1st ed. 1986).Google Scholar
  35. Brewka, G. (2001). Dynamic argument systems. A formal model of argumentation processes based on situation calculus. Journal of Logic and Computation, 11, 257–282.Google Scholar
  36. Buckingham Shum, S., & Hammond, N. (1994). Argumentation-based design rationale. What use at what cost? International Journal of Human-Computer Studies, 40(4), 603–652.Google Scholar
  37. Caminada, M. (2006). Semi-stable semantics. In P. E. Dunne & T. J. M. Bench-Capon (Eds.), Computational models of argument. Proceedings of COMMA 2006, September 11–12, 2006, Liverpool, UK (Frontiers in artificial intelligence and applications, Vol. 144). Amsterdam: IOS Press.Google Scholar
  38. Cayrol, C., & Lagasquie-Schiex, M. C. (2005). On the acceptability of arguments in bipolar argumentation frameworks. In L. Godo (Ed.), Symbolic and quantitative approaches to reasoning with uncertainty. 8th European conference, ECSQARU 2005 (pp. 378–389). Berlin: Springer.Google Scholar
  39. Chesñevar, C., McGinnis, J., Modgil, S., Rahwan, I., Reed, C., Simari, G., South, M., Vreeswijk, G., & Willmott, S. (2006). Towards an argument interchange format. Knowledge Engineering Review, 21(4), 293–316.Google Scholar
  40. Chesñevar, C. I., Simari, G. R., Alsinet, T., & Godo, L. (2004). A logic programming framework for possibilistic argumentation with vague knowledge. In Proceedings of the 20th conference on uncertainty in artificial intelligence (pp. 76–84). Arlington, VA: AUAI Press.Google Scholar
  41. Crosswhite, J., Fox, J., Reed, C. A., Scaltsas, T., & Stumpf, S. (2004). Computational models of rhetorical argument. In C. A. Reed & T. J. Norman (Eds.), Argumentation machines. New frontiers in argument and computation (pp. 175–209). Dordrecht: Kluwer.Google Scholar
  42. d’Avila Garcez, A. S., Lamb, L. C., & Gabbay, D. M. (2009). Neural-symbolic cognitive reasoning. Berlin: Springer.Google Scholar
  43. Dignum, F., Dunin-Kęplicz, B., & Verbrugge, R. (2001). Creating collective intention through dialogue. Logic Journal of the IGPL, 9(2), 305–319.Google Scholar
  44. Dung, P. M. (1995). On the acceptability of arguments and its fundamental role in non-monotonic reasoning, logic programming and n-person games. Artificial Intelligence, 77, 321–357.Google Scholar
  45. Dung, P. M., & Thang, P. M. (2010). Towards (probabilistic) argumentation for jury-based dispute resolution. In P. Baroni, F. Cerutti, M. Giacomin, & G. R. Simari (Eds.), Computational models of argument – Proceedings of COMMA 2010 (pp. 171–182). Amsterdam: Ios Press.Google Scholar
  46. Dunne, P. E. (2007). Computational properties of argument systems satisfying graph-theoretic constraints. Artificial Intelligence, 171(10), 701–729.Google Scholar
  47. Dunne, P. E., & Bench-Capon, T. J. M. (2003). Two party immediate response disputes. Properties and efficiency. Artificial Intelligence, 149(2), 221–250.Google Scholar
  48. Dworkin, R. (1978). Taking rights seriously. New impression with a reply to critics. London: Duckworth.Google Scholar
  49. van Eemeren, F. H., & Grootendorst, R. (1992a). Argumentation, communication, and fallacies. A pragma-dialectical perspective. Hillsdale: Lawrence Erlbaum (transl. into Bulgarian (2009), Chinese (1991b), French (1996), Romanian (2010), Russian (1992b), Spanish (2007)).Google Scholar
  50. van Eemeren, F. H., Grootendorst, R., & Kruiger, T. (1978). Argumentatietheorie [Argumentation theory]. Utrecht: Het Spectrum. (2nd extended ed. 1981; 3rd ed. 1986; English trans. 1984, 1987).Google Scholar
  51. van Eemeren, F. H., Grootendorst, R., & Kruiger, T. (1984). The study of argumentation. New York: Irvington. Engl. transl. by H. Lake of F. H. van Eemeren, R. Grootendorst & T. Kruiger (1981). Argumentatietheorie. 2nd ed. Utrecht: Het Spectrum. (1 st ed. 1978). (Reprinted as Eemeren, F. H. van, Grootendorst, R., & Kruiger, T. (1987). Handbook of argumentation theory. A critical survey of classical backgrounds and modern studies. Dordrecht/Providence: Foris).Google Scholar
  52. van Eemeren, F. H., & Kruiger, T. (1987). Identifying argumentation schemes. In F. H. van Eemeren, R. Grootendorst, J. A. Blair, & C. Willard (Eds.), Argumentation. Perspectives and approaches (pp. 70–81). Dordrecht: Foris.Google Scholar
  53. Egly, U., Gaggl, S. A., & Woltran, S. (2010). Answer-set programming encodings for argumentation frameworks. Argument and Computation, 1(2), 147–177.Google Scholar
  54. Elhadad, M. (1995). Using argumentation in text generation. Journal of Pragmatics, 24, 189–220.Google Scholar
  55. Falappa, M. A., Kern-Isberner, G., & Simari, G. R. (2002). Explanations, belief revision and defeasible reasoning. Artificial Intelligence, 141(1–2), 1–28.Google Scholar
  56. Fenton, N. E., Neil, M., & Lagnado, D. A. (2012). A general structure for legal arguments using Bayesian networks. Cognitive Science, advance access. http://dx.doi.org/10.1111/cogs.12004
  57. Fitelson, B. (2010). Pollock on probability in epistemology. Philosophical Studies, 148, 455–465.Google Scholar
  58. Fox, J., & Das, S. (2000). Safe and sound. Artificial intelligence in hazardous applications. Cambridge, MA: The MIT Press.Google Scholar
  59. Fox, J., & Modgil, S. (2006). From arguments to decisions. Extending the Toulmin view. In D. Hitchcock & B. Verheij (Eds.), Arguing on the Toulmin model. New essays in argument analysis and evaluation (pp. 273–287). Dordrecht: Springer.Google Scholar
  60. Gabbay, D. M., Hogger, C. J., & Robinson, J. A. (Eds.). (1994). Handbook of logic in artificial intelligence and logic programming, 3. Non-monotonic reasoning and uncertain reasoning. Oxford: Clarendon.Google Scholar
  61. García, A. J., & Simari, G. R. (2004). Defeasible logic programming. An argumentative approach. Theory and Practice of Logic Programming, 4(2), 95–138.Google Scholar
  62. Gardner, A. (1987). An artificial intelligence approach to legal reasoning. Cambridge, MA: The MIT Press.Google Scholar
  63. Garssen, B. (2001). Argument schemes. In F. H. van Eemeren (Ed.), Crucial concepts in argumentation theory (pp. 81–99). Amsterdam: Amsterdam University Press.Google Scholar
  64. van Gelder, T. (2007). The rationale for rationale. Law, Probability and Risk, 6, 23–42.Google Scholar
  65. Gelfond, M., & Lifschitz, V. (1988). The stable model semantics for logic programming. In R. A. Kowalski & K. A. Bowen (Eds.), Logic programming. Proceedings of the fifth international conference and symposium (pp. 1070–1080). Cambridge, MA: The MIT Press.Google Scholar
  66. Ginsberg, M. L. (1994). AI and non-monotonic reasoning. In D. M. Gabbay, C. J. Hogger, & J. A. Robinson (Eds.), Handbook of logic in artificial intelligence and logic programming (Non-monotonic reasoning and uncertain reasoning, Vol. 3, pp. 1–33). Oxford: Clarendon.Google Scholar
  67. Girle, R., Hitchcock, D., McBurney, P., & Verheij, B. (2004). Decision support for practical reasoning. A theoretical and computational perspective. In C. A. Reed & T. J. Norman (Eds.), Argumentation machines. New frontiers in argument and computation (pp. 55–83). Dordrecht: Kluwer.Google Scholar
  68. Gómez Lucero, M., Chesñevar, C., & Simari, G. (2009). Modelling argument accrual in possibilistic defeasible logic programming. In ECSQARU ’09 proceedings of the 10th European conference on symbolic and quantitative approaches to reasoning with uncertainty (pp. 131–143). Berlin: Springer.Google Scholar
  69. Gómez Lucero, M., Chesñevar, C., & Simari, G. (2013). Modelling argument accrual with possibilistic uncertainty in a logic programming setting. Information Sciences, 228, 1–25.Google Scholar
  70. Gordon, T. F. (1993). The pleadings game. Artificial Intelligence and Law, 2(4), 239–292.Google Scholar
  71. Gordon, T. F. (1995). The pleadings game. An artificial intelligence model of procedural justice. Dordrecht: Kluwer.Google Scholar
  72. Gordon, T. F., & Karacapilidis, N. (1997). The Zeno argumentation framework. In Proceedings of the ICAIL 1997 conference (pp. 10–18). New York: ACM Press.Google Scholar
  73. Gordon, T. F., Prakken, H., & Walton, D. (2007). The Carneades model of argument and burden of proof. Artificial Intelligence, 171, 875–896.Google Scholar
  74. Grasso, F. (2002). Towards computational rhetoric. Informal Logic, 22, 195–229.Google Scholar
  75. Grasso, F., Cawsey, A., & Jones, R. (2000). Dialectical argumentation to solve conflicts in advice giving. A case study in the promotion of healthy nutrition. International Journal of Human-Computer Studies, 53(6), 1077–1115.Google Scholar
  76. Green, N. (2007). A study of argumentation in a causal probabilistic humanistic domain. Genetic counseling. International Journal of Intelligent Systems, 22, 71–93.Google Scholar
  77. Habermas, J. (1973). Wahrheitstheorien [Theories of truth]. In H. Fahrenbach (Ed.), Wirklichkeit und Reflexion. Festschrift, für W. Schulz [Reality and reflection. Festschrift for W. Schulz] (pp. 211–265). Pfullingen: Neske.Google Scholar
  78. Hage, J. C. (1997). Reasoning with rules. An essay on legal reasoning and its underlying logic. Dordrecht: Kluwer.Google Scholar
  79. Hage, J. C. (2000). Dialectical models in artificial intelligence and law. Artificial Intelligence and Law, 8, 137–172.Google Scholar
  80. Hage, J. C. (2005). Studies in legal logic. Berlin: Springer.Google Scholar
  81. Hage, J. C., Leenes, R., & Lodder, A. R. (1993). Hard cases: A procedural approach. Artificial Intelligence and Law, 2(2), 113–167.Google Scholar
  82. Hart, H. L. A. (1951). The ascription of responsibility and rights. In A. Flew (Ed.), Logic and language. Oxford: Blackwell. (Originally Proceedings of the Aristotelian Society, 1948–1949)Google Scholar
  83. Hepler, A. B., Dawid, A. P., & Leucari, V. (2007). Object-oriented graphical representations of complex patterns of evidence. Law, Probability & Risk, 6, 275–293.Google Scholar
  84. Hitchcock, D. L. (2001). John L. Pollock’s theory of rationality. In C. W. Tindale, H. V. Hansen & E, Sveda (Eds.), Argumentation at the Century’s turn. (Proceedings of the 3rd OSSA Conference, 1999). Windsor, ON: Ontario Society for the Study of Argumentation. CD rom.Google Scholar
  85. Hitchcock, D. L. (2002). Pollock on practical reasoning. Informal Logic, 22, 247–256.Google Scholar
  86. Hofstadter, D. (1996). Metamagical themas. Questing for the essence of mind and pattern. New York: Basic Books.Google Scholar
  87. Hunter, A. (2013). A probabilistic approach to modelling uncertain logical arguments. International Journal of Approximate Reasoning, 54(1), 47–81.Google Scholar
  88. Hunter, A., & Williams, M. (2010). Qualitative evidence aggregation using argumentation. In P. Baroni, F. Cerutti, M. Giacomin, & G. R. Simari (Eds.), Computational models of argument – Proceedings of COMMA 2010 (pp. 287–298). Amsterdam: Ios Press.Google Scholar
  89. Jakobovits, H., & Vermeir, D. (1999). Robust semantics for argumentation frameworks. Journal of Logic and Computation, 9(2), 215–261.Google Scholar
  90. Jensen, F. V., & Nielsen, T. D. (2007). Bayesian networks and decision graphs. New York: Springer.Google Scholar
  91. Josephson, J. R., & Josephson, S. G. (Eds.). (1996). Abductive inference. Computation, philosophy, technology. Cambridge: Cambridge University Press.Google Scholar
  92. Karacapilidis, N., & Papadias, D. (2001). Computer supported argumentation and collaborative decision making. The HERMES system. Information Systems, 26, 259–277.Google Scholar
  93. Kienpointner, M. (1992). Alltagslogik. Struktur and Funktion von Argumentationsmustern [Everyday logic. Structure and functions of specimens of argumentation]. Stuttgart: Fromman-Holzboog.Google Scholar
  94. Kirschner, P. A., Buckingham Shum, S. J., & Carr, C. S. (Eds.). (2003). Visualizing argumentation. Software tools for collaborative and educational sense-making. London: Springer.Google Scholar
  95. Kjaerulff, U. B., & Madsen, A. L. (2008). Bayesian networks and influence diagrams. New York: Springer.Google Scholar
  96. Kowalski, R. A. (2011). Computational logic and human thinking. How to be artificially intelligent. Cambridge: Cambridge University Press.Google Scholar
  97. Kunz, W., & Rittel, H. (1970). Issues as elements of information systems (Technical Report 0131). Universität Stuttgart, Institut für Grundlagen der Planung.Google Scholar
  98. Kyburg, H. E. (1994). Uncertainty logics. In D. M. Gabbay, C. J. Hogger, & J. A. Robinson (Eds.), Handbook of logic in artificial intelligence and logic programming, 3. Non-monotonic reasoning and uncertain reasoning (pp. 397–438). Oxford: Clarendon.Google Scholar
  99. Lodder, A. R. (1999). DiaLaw. On legal justification and dialogical models of argumentation. Dordrecht: Kluwer.Google Scholar
  100. Lorenzen, P., & Lorenz, K. (1978). Dialogische Logik [Dialogical logic]. Darmstadt: Wissenschaftliche Buchgesellschaft.Google Scholar
  101. Loui, R. P. (1987). Defeat among arguments. A system of defeasible inference. Computational Intelligence, 2, 100–106.Google Scholar
  102. Loui, R. P. (1995). Hart’s critics on defeasible concepts and ascriptivism. In The fifth international conference on artificial intelligence and law. Proceedings of the conference (pp. 21–30). New York: ACM. Extended report available at http://www1.cse.wustl.edu/~loui/ail2.pdf. Accessed 10 July 2012.Google Scholar
  103. Loui, R. P. (1998). Process and policy. Resource-bounded nondemonstrative reasoning. Computational Intelligence, 14, 1–38.Google Scholar
  104. Loui, R., & Norman, J. (1995). Rationales and argument moves. Artificial Intelligence and Law, 3, 159–189.Google Scholar
  105. Loui, R., Norman, J., Altepeter, J., Pinkard, D., Craven, D., Linsday, J., & Foltz, M. A. (1997). Progress on room 5. A testbed for public interactive semi-formal legal argumentation. In Proceedings of the sixth international conference on artificial intelligence and law (pp. 207–214). New York: ACM Press.Google Scholar
  106. Mackenzie, J. D. (1979). Question-begging in non-cumulative systems. Journal of Philosophical Logic, 8, 117–133.Google Scholar
  107. Makinson, D. (1994). General patterns in non-monotonic reasoning. In D. M. Gabbay, C. J. Hogger, & J. A. Robinson (Eds.), Handbook of logic in artificial intelligence and logic programming (Non-monotonic reasoning and uncertain reasoning, Vol. 3, pp. 35–110). Oxford: Clarendon.Google Scholar
  108. McBurney, P., Hitchcock, D., & Parsons, S. (2007). The eightfold way of deliberation dialogue. International Journal of Intelligent Systems, 22, 95–132.Google Scholar
  109. McBurney, P., & Parsons, S. (2002a). Games that agents play. A formal framework for dialogues between autonomous agents. Journal for Logic, Language and Information, 11, 315–334.Google Scholar
  110. McBurney, P., & Parsons, S. (2002b). Dialogue games in multi-agent systems. Informal Logic, 22, 257–274.Google Scholar
  111. McBurney, P., & Parsons, S. (2009). Dialogue games for agent argumentation. In I. Rahwan & G. R. Simari (Eds.), Argumentation in artificial intelligence (pp. 261–280). Dordrecht: Springer.Google Scholar
  112. McCarty, L. (1977). Reflections on TAXMAN. An experiment in artificial intelligence and legal reasoning. Harvard Law Review, 90, 89–116.Google Scholar
  113. McCarty, L. (1995). An implementation of Eisner v. Macomber. In Proceedings of the fifth international conference on artificial intelligence and law (pp. 276–286). New York: ACM Press.Google Scholar
  114. Mochales Palau, R., & Moens, S. (2009). Argumentation mining. The detection, classification and structure of arguments in text. In Proceedings of the 12th international conference on artificial intelligence and law (ICAIL 2009) (pp. 98–107). New York: ACM Press.Google Scholar
  115. Modgil, S. (2005). Reasoning about preferences in argumentation frameworks. Artificial Intelligence, 173(9–10), 901–934.Google Scholar
  116. Nute, D. (1994). Defeasible logic. In D. M. Gabbay, C. J. Hogger, & J. A. Robinson (Eds.), Handbook of logic in artificial intelligence and logic programming (Non-monotonic reasoning and uncertain reasoning, Vol. 3, pp. 353–395). Oxford: Clarendon.Google Scholar
  117. Parsons, S., Sierra, C., & Jennings, N. R. (1998). Agents that reason and negotiate by arguing. Journal of Logic and Computation, 8, 261–292.Google Scholar
  118. Pearl, J. (1988). Probabilistic reasoning in intelligent systems. Networks of plausible inference. San Francisco: Morgan Kaufmann Publishers.Google Scholar
  119. Pearl, J. (2009). Causality. Models, reasoning, and inference (2nd ed.). Cambridge: Cambridge University Press (1st ed. 2000).Google Scholar
  120. Perelman, C., & Olbrechts-Tyteca, L. (1969). The new rhetoric. A treatise on argumentation. Notre Dame: University of Notre Dame Press. [trans.: Wilkinson, J. & Weaver, P. of C. Perelman and L. Olbrechts-Tyteca (1958). La nouvelle rhétorique. Traité de l’argumentation. Paris: Presses Universitaires de France].Google Scholar
  121. Pollock, J. L. (1987). Defeasible reasoning. Cognitive Science, 11, 481–518.Google Scholar
  122. Pollock, J. L. (1989). How to build a person. A prolegomenon. Cambridge, MA: The MIT Press.Google Scholar
  123. Pollock, J. L. (1994). Justification and defeat. Artificial Intelligence, 67, 377–407.Google Scholar
  124. Pollock, J. L. (1995). Cognitive carpentry. A blueprint for how to build a person. Cambridge, MA: The MIT Press.Google Scholar
  125. Pollock, J. L. (2006). Thinking about acting. Logical foundations for rational decision making. New York: Oxford University Press.Google Scholar
  126. Pollock, J. L. (2010). Defeasible reasoning and degrees of justification. Argument & Computation, 1(1), 7–22.Google Scholar
  127. Poole, D. L. (1985). On the comparison of theories. Preferring the most specific explanation. In Proceedings of the ninth international joint conference on artificial intelligence (pp. 144–147). San Francisco: Morgan Kaufmann.Google Scholar
  128. Prakken, H. (1993). Logical tools for modelling legal argument. Doctoral dissertation, Free University Amsterdam.Google Scholar
  129. Prakken, H. (1997). Logical tools for modelling legal argument. A study of defeasible reasoning in law. Dordrecht: Kluwer.Google Scholar
  130. Prakken, H. (2005a). A study of accrual of arguments, with applications to evidential reasoning. In Proceedings of the tenth international conference on artificial intelligence and law (pp. 85–94). New York: ACM Press.Google Scholar
  131. Prakken, H. (2005b). Coherence and flexibility in dialogue games for argumentation. Journal of Logic and Computation, 15, 1009–1040.Google Scholar
  132. Prakken, H. (2006). Formal systems for persuasion dialogue. The Knowledge Engineering Review, 21(2), 163–188.Google Scholar
  133. Prakken, H. (2009). Models of persuasion dialogue. In I. Rahwan & G. R. Simari (Eds.), Argumentation in artificial intelligence (pp. 281–300). Dordrecht: Springer.Google Scholar
  134. Prakken, H. (2010). An abstract framework for argumentation with structured arguments. Argument and Computation, 1, 93–124.Google Scholar
  135. Prakken, H., & Sartor, G. (1996). A dialectical model of assessing conflicting arguments in legal reasoning. Artificial Intelligence and Law, 4, 331–368.Google Scholar
  136. Prakken, H., & Sartor, G. (1998). Modelling reasoning with precedents in a formal dialogue game. Artificial Intelligence and Law, 6, 231–287.Google Scholar
  137. Prakken, H., & Sartor, G. (2007). Formalising arguments about the burden of persuasion. In Proceedings of the eleventh international conference on artificial intelligence and law (pp. 97–106). New York: ACM Press.Google Scholar
  138. Prakken, H., & Sartor, G. (2009). A logical analysis of burdens of proof. In H. Kaptein, H. Prakken, & B. Verheij (Eds.), Legal evidence and proof. Statistics, stories, logic (pp. 223–253). Farnham: Ashgate.Google Scholar
  139. Prakken, H., & Vreeswijk, G. A. W. (2002). Logics for defeasible argumentation. In D. Gabbay & F. Guenthner (Eds.), Handbook of philosophical logic (2nd ed., Vol. 4, pp. 219–318). Dordrecht: Kluwer.Google Scholar
  140. Rahwan, I., & McBurney, P. (2007). Argumentation technology. Guest editors’ introduction. IEEE Intelligent Systems, 22(6), 21–23.Google Scholar
  141. Rahwan, I., Ramchurn, S. D., Jennings, N. R., McBurney, P., Parsons, S., & Sonenberg, E. (2003). Argumentation-based negotiation. Knowledge Engineering Review, 18(4), 343–375.Google Scholar
  142. Rahwan, I., & Simari, G. R. (Eds.). (2009). Argumentation in artificial intelligence. Dordrecht: Springer.Google Scholar
  143. Rahwan, I., Zablith, F., & Reed, C. (2007). Laying the foundations for a world wide argument web. Artificial Intelligence, 171(10–15), 897–921.Google Scholar
  144. Rao, A., & Georgeff, M. (1995). BDI agents. From theory to practice. In Proceedings of the 1st international conference on multi-agent systems (pp. 312–319). Cambridge, MA: The MIT Press.Google Scholar
  145. Reed, C. A. (1999). The role of saliency in generating natural language arguments. In Proceedings of the 16th international joint conference on AI (IJCAI’99) (pp. 876–881). San Francisco: Morgan Kaufmann.Google Scholar
  146. Reed, C. A., & Grasso, F. (2007). Recent advances in computational models of natural argument. International Journal of Intelligent Systems, 22, 1–15.Google Scholar
  147. Reed, C. A., & Norman, T. J. (2004a). A roadmap of research in argument and computation. In C. A. Reed & T. J. Norman (Eds.), Argumentation machines. New frontiers in argument and computation (pp. 1–13). Dordrecht: Kluwer.Google Scholar
  148. Reed, C. A., & Norman, T. J. (Eds.). (2004b). Argumentation machines. New frontiers in argument and computation. Dordrecht: Kluwer.Google Scholar
  149. Reed, C. A., & Rowe, G. W. A. (2004). Araucaria. Software for argument analysis, diagramming and representation. International Journal on Artificial Intelligence Tools, 13, 961–979.Google Scholar
  150. Reed, C. A., & Tindale, C. W. (Eds.). (2010). Dialectics, dialogue and argumentation. An examination of Douglas Walton’s theories of reasoning. London: College Publications.Google Scholar
  151. Reiter, R. (1980). A logic for default reasoning. Artificial Intelligence, 13, 81–132.Google Scholar
  152. Rissland, E. L., & Ashley, K. D. (1987). A case-based system for trade secrets law. In Proceedings of the first international conference on artificial intelligence and law (pp. 60–66). New York: ACM Press.Google Scholar
  153. Rissland, E. L., & Ashley, K. D. (2002). A note on dimensions and factors. Artificial Intelligence and Law, 10, 65–77.Google Scholar
  154. Rittel, H., & Webber, M. (1973). Dilemmas in a general theory of planning. Policy Sciences, 4, 155–169.Google Scholar
  155. Riveret, R., Rotolo, A., Sartor, G., Prakken, H., & Roth, B. (2007). Success chances in argument games. A probabilistic approach to legal disputes. In A. R. Lodder & L. Mommers (Eds.), Legal knowledge and information systems (JURIX 2007) (pp. 99–108). Amsterdam: Ios Press.Google Scholar
  156. Roth, B. (2003). Case-based reasoning in the law. A formal theory of reasoning by case comparison. Doctoral dissertation, University of Maastricht.Google Scholar
  157. Sartor, G. (2005). Legal reasoning. A cognitive approach to the law (Treatise on legal philosophy and general jurisprudence, Vol. 5). Berlin: Springer.Google Scholar
  158. Scheuer, O., Loll, F., Pinkwart, N., & McLaren, B. M. (2010). Computer-supported argumentation. A review of the state of the art. Computer-Supported Collaborative Learning, 5, 43–102.Google Scholar
  159. Schwemmer, O., & Lorenzen, P. (1973). Konstruktive Logik, Ethik und Wissenschaftstheorie [Constructive logic, ethics and theory of science]. Mannheim: Bibliographisches Institut.Google Scholar
  160. Simari, G. R., & Loui, R. P. (1992). A mathematical treatment of defeasible reasoning and its applications. Artificial Intelligence, 53, 125–157.Google Scholar
  161. Suthers, D. (1999). Representational support for collaborative inquiry. In Proceedings of the 32nd Hawaii international conference on the system sciences (HICSS-32). Los Alamitos, CA: Institute of Electrical and Electronics Engineers (IEEE). Google Scholar
  162. Suthers, D., Weiner, A., Connelly, J., & Paolucci, M. (1995). Belvedere. Engaging students in critical discussion of science and public policy issues. In Proceedings of the 7th world conference on artificial intelligence in education (AIED ’95). Charlottesville, VA: Association for the Advancement of Computing in Education, pp. 266–273.Google Scholar
  163. Sycara, K. (1989). Argumentation. Planning other agents’ plans. In Proceedings of the eleventh international joint conference on artificial intelligence (pp. 517–523). San Francisco: Morgan Kaufmann Publishers.Google Scholar
  164. Talbott, W. (2011). Bayesian epistemology. In E. N. Zalta (Ed.), The Stanford encyclopedia of philosophy (Summer 2011 ed.). http://plato.stanford.edu/archives/sum2011/entries/epistemology-bayesian/
  165. Taroni, F., Aitken, C., Garbolino, P., & Biedermann, A. (2006). Bayesian networks and probabilistic inference in forensic science. Chichester: Wiley.Google Scholar
  166. Teufel, S. (1999). Argumentative zoning. Information extraction from scientific articles. Doctoral dissertation, University of Edinburgh.Google Scholar
  167. Thagard, P. (1992). Conceptual revolutions. Princeton: Princeton University Press.Google Scholar
  168. Toulmin, S. E. (2003). The uses of argument. Cambridge: Cambridge University Press (1st ed. 1958).Google Scholar
  169. Verheij, B. (1996a). Rules, reasons, arguments. Formal studies of argumentation and defeat. Doctoral dissertation, University of Maastricht.Google Scholar
  170. Verheij, B. (1996b). Two approaches to dialectical argumentation. Admissible sets and argumentation stages. In J.-J. C. Meyer & L. C. van der Gaag (Eds.), NAIC’96. Proceedings of the eighth Dutch conference on artificial intelligence (pp. 357–368). Utrecht: Utrecht University.Google Scholar
  171. Verheij, B. (2003a). DefLog. On the logical interpretation of prima facie justified assumptions. Journal of Logic and Computation, 13(3), 319–346.Google Scholar
  172. Verheij, B. (2003b). Dialectical argumentation with argumentation schemes. An approach to legal logic. Artificial Intelligence and Law, 11(1–2), 167–195.Google Scholar
  173. Verheij, B. (2005a). Evaluating arguments based on Toulmin’s scheme. Argumentation, 19, 347–371. [Reprinted in Hitchcock, D. L., & Verheij, B. (Eds.). (2006), Arguing on the Toulmin model. New essays in argument analysis and evaluation (pp. 181–202). Dordrecht: Springer].Google Scholar
  174. Verheij, B. (2005b). Virtual arguments. On the design of argument assistants for lawyers and other arguers. The Hague: T. M. C. Asser Press.Google Scholar
  175. Verheij, B. (2007). A labeling approach to the computation of credulous acceptance in argumentation. In M. M. Veloso (Ed.), IJCAI 2007, Proceedings of the 20th international joint conference on artificial intelligence (pp. 623–628). San Francisco: Morgan Kaufmann Publishers.Google Scholar
  176. Verheij, B. (2012). Jumping to conclusions. A logico-probabilistic foundation for defeasible rule-based arguments. In L. Fariñas del Cerro, A. Herzig, & J. Mengin (Eds.), Logics in artificial intelligence. 13th European conference, JELIA 2012. Toulouse, France, September 2012. Proceedings (LNAI, Vol. 7519, pp. 411–423). Berlin: Springer.Google Scholar
  177. Verheij, B., Hage, J. C., & van den Herik, H. J. (1998). An integrated view on rules and principles. Artificial Intelligence and Law, 6(1), 3–26.Google Scholar
  178. Vreeswijk, G. A. W. (1993). Studies in defeasible argumentation. Doctoral dissertation, Free University, Amsterdam.Google Scholar
  179. Vreeswijk, G. A. W. (1995a). Formalizing nomic. Working on a theory of communication with modifiable rules of procedure (Technical Report CS 95–02). Maastricht: Vakgroep Informatica (FdAW), Rijksuniversiteit Limburg. http://arno.unimaas.nl/show.cgi?fid=126
  180. Vreeswijk, G. A. W. (1995b). The computational value of debate in defeasible reasoning. Argumentation, 9, 305–342.Google Scholar
  181. Vreeswijk, G. (1997). Abstract argumentation systems. Artificial Intelligence, 90, 225–279.Google Scholar
  182. Vreeswijk, G. A. W. (2000). Representation of formal dispute with a standing order. Artificial Intelligence and Law, 8, 205–231.Google Scholar
  183. Walton, D. N., & Krabbe, E. C. W. (1995). Commitment in dialogue. Basic concepts of interpersonal reasoning. Albany: State University of New York Press.Google Scholar
  184. Walton, D. N., Reed, C. A., & Macagno, F. (2008). Argumentation schemes. Cambridge: Cambridge University Press.Google Scholar
  185. Wooldridge, M. (2009). An introduction to multiagent systems. Chichester: Wiley.Google Scholar
  186. Zukerman, I., McConachy, R., & Korb, K. (1998). Bayesian reasoning in an abductive mechanism for argument generation and analysis. In Proceedings of the fifteenth national conference on artificial intelligence (AAAI-98, Madison) (pp. 833–838). Menlo Park: AAAI Press.Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  • Frans H. van Eemeren
    • 1
  • Bart Garssen
    • 1
  • Erik C. W. Krabbe
    • 2
  • A. Francisca Snoeck Henkemans
    • 1
  • Bart Verheij
    • 3
  • Jean H. M. Wagemans
    • 1
  1. 1.University of AmsterdamAmsterdamThe Netherlands
  2. 2.University of GroningenGroningenThe Netherlands
  3. 3.Faculty of Mathematics and Natural SciencesUniversity of GroningenGroningenThe Netherlands

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