94 research outputs found

    Towards modelling dialectic and eristic argumentation on the social web

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    Modelling arguments on the social web is a key challenge for those studying computational argumentation. This is because formal models of argumentation tend to assume dialectic and logical argument, whereas argumentation on the social web is highly eristic. In this paper we explore this gap by bringing together the Argument Interchange Format (AIF) and the Semantic Interlinked Online Communities (SIOC) project, and modelling a sample of social web arguments. This allows us to explore which eristic effects cannot be modelled, and also to see which features of the social web are missing.We show that even in our small sample, from YouTube, Twitter and Facebook, eristic effects (such as playing to the audience) were missing from the final model, and that key social features (such as likes and dislikes) were also not represented. This suggests that both eristic and social extensions need to be made to our models of argumentation in order to deal effectively with the social we

    Introduction to the Special Issue: The AgentLink III Technical Forums

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    This article introduces the special issue of ACM Transactions on Autonomous and Adaptive Systems devoted to research papers arising from the three Technical Forum Group meetings held in 2004 and 2005 that were organized and sponsored by the European FP6 Coordination Action AgentLink III

    Dealing with Qualitative and Quantitative Features in Legal Domains

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    In this work, we enrich a formalism for argumentation by including a formal characterization of features related to the knowledge, in order to capture proper reasoning in legal domains. We add meta-data information to the arguments in the form of labels representing quantitative and qualitative data about them. These labels are propagated through an argumentative graph according to the relations of support, conflict, and aggregation between arguments.Comment: arXiv admin note: text overlap with arXiv:1903.0186

    Teaching in Ill-Defined Domains Using ITS and AI Appraoches

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    Ill-defined domains offer many challenges to computer scientists. Developing intelligent tutoring systems (ITSs) in these domains is a very challenging task due to the difficulty in modeling these domains, answers to ill-defined problems are ambiguously identified as right or wrong, and no generally accepted architecture is currently existed. This paper presents general guidelines for the development of ITSs in ill-defined domains, such as Argumentation and Ethics. This is instantiated in the two example systems AEINS and ALES. These systems offer adaptive learning processes and personalized feedback aiming to transfer the required skills to the learners and develop their reasoning

    Intertextual correspondence for integrating corpora

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