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.
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.
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.
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.
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.
See the entry on nonmonotonic logic in the Stanford Encyclopedia of Philosophy at http://plato.stanford.edu/entries/logic-nonmonotonic/ (Antonelli 2010).
- 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.
- 8.
Pollock aims for a theory of projectible properties. See also Pollock (1995, p. 66f).
- 9.
- 10.
- 11.
Prakken (2010) speaks of ways of attack, where argument defeat is the result of argument attack.
- 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.
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.
This is especially helpful when also supporting connections are considered; see Sect. 11.5.
- 15.
In the following, we make use of terminology proposed by Verheij (2007).
- 16.
- 17.
Dung’s own definition of grounded extension, which does not use labelling, is not discussed here.
- 18.
He believes that a projectibility constraint is required (1995, pp. 105–106). See Note 8.
- 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.
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.
http://en.wikipedia.org/wiki/Nomic. See also Hofstadter (1996, chapter 4).
- 22.
See also the study of Nomic by Vreeswijk (1995a).
- 23.
For an overview of the field of multi-agent systems, see the textbook by Wooldridge (2009), which contains a chapter entitled “Arguing.”
- 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.
A systematic overview of argumentation dialogue models of negotiation has been provided by Rahwan et al. (2003).
- 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.
The book is based on Prakken’s (1993) doctoral dissertation.
- 28.
“∀x …” stands for “for every entity x it holds that ….” Similarly, for “∀y ….” See also Sect. 6.2 of this volume.
- 29.
Reason-based logic exists in a series of versions, some introduced in collaboration with Verheij (e.g., Verheij 1996a).
- 30.
We shall simplify Hage’s formalism a bit by omitting the explicit distinction between rules and principles.
- 31.
- 32.
The example is inspired by the case material used by Roth (2003).
- 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.
The book’s subtitle adds modestly: A Prolegomenon.
- 35.
- 36.
References
Aleven, V. (1997). Teaching case-based reasoning through a model and examples. Doctoral dissertation, University of Pittsburgh.
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.
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.
Alexy, R. (1978). Theorie der juristischen Argumentation [Theory of legal argumentation]. Frankfurt am Main: Suhrkamp Verlag.
Amgoud, L. (2009). Argumentation for decision making. In I. Rahwan & G. R. Simari (Eds.), Argumentation in artificial intelligence (pp. 301–320). Dordrecht: Springer.
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.
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/
Antoniou, G., Billington, D., Governatori, G., & Maher, M. (2001). Representation results for defeasible logic. ACM Transactions on Computational Logic, 2(2), 255–287.
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.
Ashley, K. D. (1990). Modeling legal argument. Reasoning with cases and hypotheticals. Cambridge, MA: The MIT Press.
Atkinson, K. (2012). Introduction to special issue on modelling Popov v. Hayashi. Artificial Intelligence and Law, 20, 1–14.
Atkinson, K., & Bench-Capon, T. J. M. (2007). Practical reasoning as presumptive argumentation using action based alternating transition systems. Artificial Intelligence, 171, 855–874.
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.
Atkinson, K., Bench-Capon, T. J. M., & McBurney, P. (2006). Computational representation of practical argument. Synthese, 152, 157–206.
Baroni, P., Caminada, M., & Giacomin, M. (2011). An introduction to argumentation semantics. Knowledge Engineering Review, 26(4), 365–410.
Barth, E. M., & Krabbe, E. C. W. (1982). From axiom to dialogue. A philosophical study of logics and argumentation. Berlin: de Gruyter.
Bench-Capon, T. J. M. (2003). Persuasion in practical argument using value-based argumentation frameworks. Journal of Logic and Computation, 13(3), 429–448.
Bench-Capon, T. J. M., & Dunne, P. E. (2007). Argumentation in artificial intelligence. Artificial Intelligence, 171, 619–641.
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.
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.
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.
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.
Bench-Capon, T. J. M., & Sartor, G. (2003). A model of legal reasoning with cases incorporating theories and values. Artificial Intelligence, 150, 97–143.
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.
Besnard, P., & Hunter, A. (2008). Elements of argumentation. Cambridge, MA: The MIT Press.
Bex, F. J. (2011). Arguments, stories and criminal evidence. A formal hybrid theory. Dordrecht: Springer.
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.
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.
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.
Bondarenko, A., Dung, P. M., Kowalski, R. A., & Toni, F. (1997). An abstract, argumentation-theoretic approach to default reasoning. Artificial Intelligence, 93, 63–101.
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.
Branting, L. K. (1991). Building explanations from rules and structured cases. International Journal of Man–Machine Studies, 34, 797–837.
Branting, L. K. (2000). Reasoning with rules and precedents. A computational model of legal analysis. Dordrecht: Kluwer.
Bratko, I. (2001). PROLOG. Programming for artificial intelligence (3rd ed.). Harlow: Pearson (1st ed. 1986).
Brewka, G. (2001). Dynamic argument systems. A formal model of argumentation processes based on situation calculus. Journal of Logic and Computation, 11, 257–282.
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.
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.
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.
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.
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.
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.
d’Avila Garcez, A. S., Lamb, L. C., & Gabbay, D. M. (2009). Neural-symbolic cognitive reasoning. Berlin: Springer.
Dignum, F., Dunin-Kęplicz, B., & Verbrugge, R. (2001). Creating collective intention through dialogue. Logic Journal of the IGPL, 9(2), 305–319.
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.
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.
Dunne, P. E. (2007). Computational properties of argument systems satisfying graph-theoretic constraints. Artificial Intelligence, 171(10), 701–729.
Dunne, P. E., & Bench-Capon, T. J. M. (2003). Two party immediate response disputes. Properties and efficiency. Artificial Intelligence, 149(2), 221–250.
Dworkin, R. (1978). Taking rights seriously. New impression with a reply to critics. London: Duckworth.
van Eemeren, F. H., Grootendorst, R., & Kruiger, T. (1978). Argumentatietheorie [Argumentation theory]. Utrecht: Het Spectrum. (2nd extended ed. 1981; 3rd ed. 1986; English transl. 1984, 1987).
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).
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).).
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.
Egly, U., Gaggl, S. A., & Woltran, S. (2010). Answer-set programming encodings for argumentation frameworks. Argument and Computation, 1(2), 147–177.
Elhadad, M. (1995). Using argumentation in text generation. Journal of Pragmatics, 24, 189–220.
Falappa, M. A., Kern-Isberner, G., & Simari, G. R. (2002). Explanations, belief revision and defeasible reasoning. Artificial Intelligence, 141(1–2), 1–28.
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
Fitelson, B. (2010). Pollock on probability in epistemology. Philosophical Studies, 148, 455–465.
Fox, J., & Das, S. (2000). Safe and sound. Artificial intelligence in hazardous applications. Cambridge, MA: The MIT Press.
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.
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.
García, A. J., & Simari, G. R. (2004). Defeasible logic programming. An argumentative approach. Theory and Practice of Logic Programming, 4(2), 95–138.
Gardner, A. (1987). An artificial intelligence approach to legal reasoning. Cambridge, MA: The MIT Press.
Garssen, B. (2001). Argument schemes. In F. H. van Eemeren (Ed.), Crucial concepts in argumentation theory (pp. 81–99). Amsterdam: Amsterdam University Press.
van Gelder, T. (2007). The rationale for rationale. Law, Probability and Risk, 6, 23–42.
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.
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.
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.
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.
Gómez Lucero, M., Chesñevar, C., & Simari, G. (2013). Modelling argument accrual with possibilistic uncertainty in a logic programming setting. Information Sciences, 228, 1–25.
Gordon, T. F. (1993). The pleadings game. Artificial Intelligence and Law, 2(4), 239–292.
Gordon, T. F. (1995). The pleadings game. An artificial intelligence model of procedural justice. Dordrecht: Kluwer.
Gordon, T. F., & Karacapilidis, N. (1997). The Zeno argumentation framework. In Proceedings of the ICAIL 1997 conference (pp. 10–18). New York: ACM Press.
Gordon, T. F., Prakken, H., & Walton, D. (2007). The Carneades model of argument and burden of proof. Artificial Intelligence, 171, 875–896.
Grasso, F. (2002). Towards computational rhetoric. Informal Logic, 22, 195–229.
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.
Green, N. (2007). A study of argumentation in a causal probabilistic humanistic domain. Genetic counseling. International Journal of Intelligent Systems, 22, 71–93.
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.
Hage, J. C. (1997). Reasoning with rules. An essay on legal reasoning and its underlying logic. Dordrecht: Kluwer.
Hage, J. C. (2000). Dialectical models in artificial intelligence and law. Artificial Intelligence and Law, 8, 137–172.
Hage, J. C. (2005). Studies in legal logic. Berlin: Springer.
Hage, J. C., Leenes, R., & Lodder, A. R. (1993). Hard cases: A procedural approach. Artificial Intelligence and Law, 2(2), 113–167.
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)
Hepler, A. B., Dawid, A. P., & Leucari, V. (2007). Object-oriented graphical representations of complex patterns of evidence. Law, Probability & Risk, 6, 275–293.
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.
Hitchcock, D. L. (2002). Pollock on practical reasoning. Informal Logic, 22, 247–256.
Hofstadter, D. (1996). Metamagical themas. Questing for the essence of mind and pattern. New York: Basic Books.
Hunter, A. (2013). A probabilistic approach to modelling uncertain logical arguments. International Journal of Approximate Reasoning, 54(1), 47–81.
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.
Jakobovits, H., & Vermeir, D. (1999). Robust semantics for argumentation frameworks. Journal of Logic and Computation, 9(2), 215–261.
Jensen, F. V., & Nielsen, T. D. (2007). Bayesian networks and decision graphs. New York: Springer.
Josephson, J. R., & Josephson, S. G. (Eds.). (1996). Abductive inference. Computation, philosophy, technology. Cambridge: Cambridge University Press.
Karacapilidis, N., & Papadias, D. (2001). Computer supported argumentation and collaborative decision making. The HERMES system. Information Systems, 26, 259–277.
Kienpointner, M. (1992). Alltagslogik. Struktur and Funktion von Argumentationsmustern [Everyday logic. Structure and functions of specimens of argumentation]. Stuttgart: Fromman-Holzboog.
Kirschner, P. A., Buckingham Shum, S. J., & Carr, C. S. (Eds.). (2003). Visualizing argumentation. Software tools for collaborative and educational sense-making. London: Springer.
Kjaerulff, U. B., & Madsen, A. L. (2008). Bayesian networks and influence diagrams. New York: Springer.
Kowalski, R. A. (2011). Computational logic and human thinking. How to be artificially intelligent. Cambridge: Cambridge University Press.
Kunz, W., & Rittel, H. (1970). Issues as elements of information systems (Technical Report 0131). Universität Stuttgart, Institut für Grundlagen der Planung.
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.
Lodder, A. R. (1999). DiaLaw. On legal justification and dialogical models of argumentation. Dordrecht: Kluwer.
Lorenzen, P., & Lorenz, K. (1978). Dialogische Logik [Dialogical logic]. Darmstadt: Wissenschaftliche Buchgesellschaft.
Loui, R. P. (1987). Defeat among arguments. A system of defeasible inference. Computational Intelligence, 2, 100–106.
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.
Loui, R. P. (1998). Process and policy. Resource-bounded nondemonstrative reasoning. Computational Intelligence, 14, 1–38.
Loui, R., & Norman, J. (1995). Rationales and argument moves. Artificial Intelligence and Law, 3, 159–189.
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.
Mackenzie, J. D. (1979). Question-begging in non-cumulative systems. Journal of Philosophical Logic, 8, 117–133.
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.
McBurney, P., Hitchcock, D., & Parsons, S. (2007). The eightfold way of deliberation dialogue. International Journal of Intelligent Systems, 22, 95–132.
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.
McBurney, P., & Parsons, S. (2002b). Dialogue games in multi-agent systems. Informal Logic, 22, 257–274.
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.
McCarty, L. (1977). Reflections on TAXMAN. An experiment in artificial intelligence and legal reasoning. Harvard Law Review, 90, 89–116.
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.
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.
Modgil, S. (2005). Reasoning about preferences in argumentation frameworks. Artificial Intelligence, 173(9–10), 901–934.
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.
Parsons, S., Sierra, C., & Jennings, N. R. (1998). Agents that reason and negotiate by arguing. Journal of Logic and Computation, 8, 261–292.
Pearl, J. (1988). Probabilistic reasoning in intelligent systems. Networks of plausible inference. San Francisco: Morgan Kaufmann Publishers.
Pearl, J. (2009). Causality. Models, reasoning, and inference (2nd ed.). Cambridge: Cambridge University Press (1st ed. 2000).
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].
Pollock, J. L. (1987). Defeasible reasoning. Cognitive Science, 11, 481–518.
Pollock, J. L. (1989). How to build a person. A prolegomenon. Cambridge, MA: The MIT Press.
Pollock, J. L. (1994). Justification and defeat. Artificial Intelligence, 67, 377–407.
Pollock, J. L. (1995). Cognitive carpentry. A blueprint for how to build a person. Cambridge, MA: The MIT Press.
Pollock, J. L. (2006). Thinking about acting. Logical foundations for rational decision making. New York: Oxford University Press.
Pollock, J. L. (2010). Defeasible reasoning and degrees of justification. Argument & Computation, 1(1), 7–22.
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.
Prakken, H. (1993). Logical tools for modelling legal argument. Doctoral dissertation, Free University Amsterdam.
Prakken, H. (1997). Logical tools for modelling legal argument. A study of defeasible reasoning in law. Dordrecht: Kluwer.
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.
Prakken, H. (2005b). Coherence and flexibility in dialogue games for argumentation. Journal of Logic and Computation, 15, 1009–1040.
Prakken, H. (2006). Formal systems for persuasion dialogue. The Knowledge Engineering Review, 21(2), 163–188.
Prakken, H. (2009). Models of persuasion dialogue. In I. Rahwan & G. R. Simari (Eds.), Argumentation in artificial intelligence (pp. 281–300). Dordrecht: Springer.
Prakken, H. (2010). An abstract framework for argumentation with structured arguments. Argument and Computation, 1, 93–124.
Prakken, H., & Sartor, G. (1996). A dialectical model of assessing conflicting arguments in legal reasoning. Artificial Intelligence and Law, 4, 331–368.
Prakken, H., & Sartor, G. (1998). Modelling reasoning with precedents in a formal dialogue game. Artificial Intelligence and Law, 6, 231–287.
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.
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.
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.
Rahwan, I., & McBurney, P. (2007). Argumentation technology. Guest editors’ introduction. IEEE Intelligent Systems, 22(6), 21–23.
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.
Rahwan, I., & Simari, G. R. (Eds.). (2009). Argumentation in artificial intelligence. Dordrecht: Springer.
Rahwan, I., Zablith, F., & Reed, C. (2007). Laying the foundations for a world wide argument web. Artificial Intelligence, 171(10–15), 897–921.
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.
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.
Reed, C. A., & Grasso, F. (2007). Recent advances in computational models of natural argument. International Journal of Intelligent Systems, 22, 1–15.
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.
Reed, C. A., & Norman, T. J. (Eds.). (2004b). Argumentation machines. New frontiers in argument and computation. Dordrecht: Kluwer.
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.
Reed, C. A., & Tindale, C. W. (Eds.). (2010). Dialectics, dialogue and argumentation. An examination of Douglas Walton’s theories of reasoning. London: College Publications.
Reiter, R. (1980). A logic for default reasoning. Artificial Intelligence, 13, 81–132.
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.
Rissland, E. L., & Ashley, K. D. (2002). A note on dimensions and factors. Artificial Intelligence and Law, 10, 65–77.
Rittel, H., & Webber, M. (1973). Dilemmas in a general theory of planning. Policy Sciences, 4, 155–169.
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.
Roth, B. (2003). Case-based reasoning in the law. A formal theory of reasoning by case comparison. Doctoral dissertation, University of Maastricht.
Sartor, G. (2005). Legal reasoning. A cognitive approach to the law (Treatise on legal philosophy and general jurisprudence, Vol. 5). Berlin: Springer.
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.
Schwemmer, O., & Lorenzen, P. (1973). Konstruktive Logik, Ethik und Wissenschaftstheorie [Constructive logic, ethics and theory of science]. Mannheim: Bibliographisches Institut.
Simari, G. R., & Loui, R. P. (1992). A mathematical treatment of defeasible reasoning and its applications. Artificial Intelligence, 53, 125–157.
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).
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.
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.
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/
Taroni, F., Aitken, C., Garbolino, P., & Biedermann, A. (2006). Bayesian networks and probabilistic inference in forensic science. Chichester: Wiley.
Teufel, S. (1999). Argumentative zoning. Information extraction from scientific articles. Doctoral dissertation, University of Edinburgh.
Thagard, P. (1992). Conceptual revolutions. Princeton: Princeton University Press.
Toulmin, S. E. (2003). The uses of argument. Cambridge: Cambridge University Press (1st ed. 1958).
Verheij, B. (1996a). Rules, reasons, arguments. Formal studies of argumentation and defeat. Doctoral dissertation, University of Maastricht.
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.
Verheij, B. (2003a). DefLog. On the logical interpretation of prima facie justified assumptions. Journal of Logic and Computation, 13(3), 319–346.
Verheij, B. (2003b). Dialectical argumentation with argumentation schemes. An approach to legal logic. Artificial Intelligence and Law, 11(1–2), 167–195.
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].
Verheij, B. (2005b). Virtual arguments. On the design of argument assistants for lawyers and other arguers. The Hague: T. M. C. Asser Press.
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.
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.
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.
Vreeswijk, G. A. W. (1993). Studies in defeasible argumentation. Doctoral dissertation, Free University, Amsterdam.
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
Vreeswijk, G. A. W. (1995b). The computational value of debate in defeasible reasoning. Argumentation, 9, 305–342.
Vreeswijk, G. (1997). Abstract argumentation systems. Artificial Intelligence, 90, 225–279.
Vreeswijk, G. A. W. (2000). Representation of formal dispute with a standing order. Artificial Intelligence and Law, 8, 205–231.
Walton, D. N., & Krabbe, E. C. W. (1995). Commitment in dialogue. Basic concepts of interpersonal reasoning. Albany: State University of New York Press.
Walton, D. N., Reed, C. A., & Macagno, F. (2008). Argumentation schemes. Cambridge: Cambridge University Press.
Wooldridge, M. (2009). An introduction to multiagent systems. Chichester: Wiley.
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.
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van Eemeren, F.H., Garssen, B., Krabbe, E.C.W., Snoeck Henkemans, A.F., Verheij, B., Wagemans, J.H.M. (2020). Argumentation and Artificial Intelligence. In: Handbook of Argumentation Theory. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-6883-3_11-2
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