The rationale for RationaleTM

Gelder, Tim van

doi:10.1093/lpr/mgm032

Cited by 85 publications

(44 citation statements)

References 11 publications

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“…The second answer is that our long-term goal is to use the framework we describe as the basis for a decision support tool that helps human users to understand the impact of trust on their decisions. Previous work on graphical representation of arguments, for example [10,49,66,76,80,88,89], suggests that a graphical representation will be a good way to do this, and here we have a graphical representation that can exactly mirror the computation of acceptability of arguments. An example of a trust-extended argument graph is shown in Figure 5.…”

Section: Trust-extended Argumentationmentioning

confidence: 98%

Using argumentation to reason about trust and belief

Tang

Cai

McBurney

et al. 2011

Journal of Logic and Computation

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Trust is a mechanism for managing the uncertainty about autonomous entities and the information they store, and so can play an important role in any decentralized system. As a result, trust has been widely studied in multiagent systems and related fields such as the semantic web. Here we introduce a formal system of argumentation that can be used to reason using information about trust. This system is described as a set of graphs, which makes it possible to combine our approach with conventional representations of trust between individuals where the relationships between individuals are given in the form of a graph. The resulting system can easily relate the grounds of an argument to the agent that supplied the information, and can be used as the basis to compute Dungian notions of acceptability that take trust into account. We explore some of the properties of these argumentation graphs, examine the computation of trust and belief in the graphs, and illustrate the capabilities of the system on an example from the trust literature.

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Section: Trust-extended Argumentationmentioning

confidence: 98%

Using argumentation to reason about trust and belief

Tang

Cai

McBurney

et al. 2011

Journal of Logic and Computation

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“…Furthermore, because the AIF serves as a theory-neutral ontology of argumentation, it facilitates the 'translation' of other computational and non-computational approaches to argumentation in order to allow interaction with the components of the Argument Web. In this way, it becomes possible to, for example, analyse an argumentative text using Rationale [50], save the result in AIFdb [24], and then diagram it in Carneades [10]. Strengths of different theoretical approaches can thus be exploited, and a lack of functionality of one tool can be overcome by using another.…”

Section: A Computational Approach To Dialogical Fallaciesmentioning

confidence: 99%

A Critical Discussion Game for Prohibiting Fallacies

Visser

Budzyńska

Reed

2017

LLP

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Abstract. The study of fallacies is at the heart of argumentation studies. In response to Hamblin's devastating critique of the state of the theory of fallacies in 1970, both formal dialectical and informal approaches to fallacies developed. In the current paper, we focus on an influential informal approach to fallacies, part of the pragma-dialectical theory of argumentation. Central to the pragma-dialectical method for analysing and evaluating argumentative discourse is the ideal model of a critical discussion. In this discussion model, a dialectical perspective on argumentation is combined with a pragmatic take on communicative interaction. By formalising and computationally implementing the model of a critical discussion, we take a first step in the development of software to computationally model argumentative dialogue in which fallacies are prohibited along the pragmadialectical norms. We do this by defining the Critical Discussion Game, a formal dialogue game based on the pragma-dialectical discussion model, executable on an online user-interface which is part of a larger infrastructure of argumentation software.

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“…This assumption is supported by around 95% of the argument analyses contained in AIFdb (Lawrence et al, 2012) as well as the fact that many manual analysis tools including Araucaria (Reed and Rowe, 2004), iLogos 3 , Rationale (Van Gelder, 2007) and Carneades (Gordon et al, 2007), limit the user to a tree format.…”

Section: Structure Identificationmentioning

confidence: 99%