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Titlebook: Argumentation in Artificial Intelligence; Guillermo Simari,Iyad Rahwan Book 2009 Springer-Verlag US 2009 Bayesian network.agents.argument

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Henry Pigot,Kristian Soltesz,Stig Steenmming [9, 7], with its dialectical interpretation of the acceptability of negation-as-failure assumptions based on the notion of “no-evidence-to-the-contrary” [9, 7], by the Kakas, Kowalski and Toni interpretation of the preferred extension semantics in argumentation-theoretic terms [24, 25], and by Dung’s abstract argumentation (AA) [6, 8].
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https://doi.org/10.1007/978-1-0716-3846-0ds. —by a nice numerological coincidence—1958) continues to grow (see [7] and the special issue of . 2005; Vol. 19, No. 3). Also the field of Artificial Intelligence has discovered Toulmin’s work. Especially four of Toulmin’s themes have found follow-up in Artificial Intelligence.
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https://doi.org/10.1007/978-1-4939-8597-5or their argumentative goals to be achieved. What we mean by arguing strategically is that instead of making arbitrary arguments, an agent would carefully choose its argumentative moves in order to further its own objectives.
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Argumentation Theory: A Very Short Introduction,alled fallacies, and the logic textbooks have for over 2000 years tried to help students to identify these fallacies, and to deal with them when they are encountered. The problem was that deductive logic did not seem to be much use for this purpose, and there seemed to be no other obvious formal structure that could usefully be applied to them.
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Abstract Argumentation and Valueserred extensions establishes that its claim must be accepted as true, and membership of at least one preferred extension shows that the claim is at least tenable. In consequence, that admissible sets of arguments are conflict free is an important requirement under all the various semantics.
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Bipolar abstract argumentation systemsack another argument, but it can also support another one. This suggests a notion of bipolarity, .. the existence of two independent kinds of information which have a diametrically opposed nature and which represent repellent forces.
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The Toulmin Argument Model in Artificial Intelligenceds. —by a nice numerological coincidence—1958) continues to grow (see [7] and the special issue of . 2005; Vol. 19, No. 3). Also the field of Artificial Intelligence has discovered Toulmin’s work. Especially four of Toulmin’s themes have found follow-up in Artificial Intelligence.
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